• 专利附录
  • 专利说明
  • 权利要求
  • 类似技术
  • 同族专利
  • 引用文献
  • 法律状态
  • 优先权
    • 专利摘要:
    DEFECTING DEFECTS IN HVAC SYSTEMS USING CONSTRUCTION INFORMATION MODELS AND THERMAL FLOW MODELS. The present invention relates to systems and methods that provide a Thermal Flow Model (HFM) graph modeling methodology. The modalities automatically translate the descriptions of the formal HVAC system from a Building Information Model (BIM) into HFM graphics, and compile the graphics for executable FDD systems. During an engineering phase, a user interface is used to enter parameters, conditions and switches not found in BIM. During the execution phase, real-time data from an HVAC control system is inserted into the generated FDD system (HFM graph) for defect detection and diagnosis.
    公开号:BR112012009211B1
    申请号:R112012009211-1
    申请日:2010-10-19
    公开日:2020-11-03
    发明作者:Gerhard Zimmermann；Yan Lu；George Lo
    申请人:Siemens Corporation；
    IPC主号:
    专利说明:

    Background of the Invention
    [001] This invention relates in general to process control. Specifically, this invention relates to a Thermal Flow Model (HFM) for the detection and diagnosis of HVAC system defects. The modalities use the modularity of graphics to achieve a direct automated mapping of HVAC structures and components to HFM graphics, and use of node behavior models and software libraries to translate HFM graphics to systems that can be integrated into HVAC control systems. .
    [002] Modern Heating, Ventilation and Air Conditioning (HVAC) control systems are often too complex to have their own means for Defect Detection and Diagnosis (FDD) and effective correction. With many existing FDD approaches, engineering efforts to apply and adapt them to various HVAC systems are large.
    [003] HVAC construction control and mechanical construction systems are prone to many defects that cause failure. Some failures lead to alarms, others decrease energy efficiency, system life and user comfort without obvious notifications. Even if faults are detected by a maintenance staff, troubleshooting is often very difficult because there is no one-to-one correspondence between the defects and the reported faults.
    [004] The FDD for HVAC has become an important topic with many contributions from academics and the industry. However, a problem still remains when the research results are put into practice, since almost all buildings are different there are a wide variety of HVAC systems. If the FDD system does not match a particular HVAC system, they are not detected. And if customized for a specific system, the development effort and cost may be too high in relation to the possible gain.
    [005] The use of specialist systems to diagnose defects in HVAC components and systems has been tried and include rule-based methods, strategies based on a “fuzzy” model, and classifiers based on Artificial Neural Network (ANN).
    [006] Usually a set of fault rules based on inadequacies of temperature or pressure is derived to detect defects. In most studies, either the rules were derived manually for each specific HVAC system or ANN must be trained offline which may not necessarily cover all defects due to limited training data.
    [007] These processes are time-consuming and laborious. As the recent advancement in construction modeling technology has already significantly impacted the construction design and construction engineering process, it is believed that developing an FDD system based on construction information models will both increase the fault-finding capacity of construction and reducing the engineering process for generating defect rules.
    [008] Multilevel Flow Models (MFM) have been applied to power plants and similar systems. Flow models are flows of information and energy, graphs representing mass. Once the graphs are established, mass-based rules and energy conservation laws are extracted and analyzed by an inference engine to perform FDD in real time.
    [009] MFM flow models are graphs that represent mass, information and energy flows where the laws of conservation of mass and energy apply and can be used for defect detection and diagnosis. However, to use MFM, flows must be measured, which are not often available for an HVAC system.
    [0010] A system and method that provides FDD to reduce this effort is desired. Summary of the Invention
    [0011] It was discovered that it would be desirable to have systems and methods that provide a Thermal Flow Model (HFM) methodology. The modalities automatically translate the formal HVAC system descriptions of a Building Information Model (BIM) into HFM charts, and compile the charts for executable FDD systems. During an engineering phase, a Graphical User Interface (GUI) is used to configure parameters, conditions, and logic switches not found in BIM. During an execution phase, real-time data from the HVAC control system is inserted into the generated FDD system (HFM graph) for defect detection and diagnosis.
    [0012] The modalities create a hierarchical graphic HFM model that can be used to generate automatic HVAC FDD from appropriate BIMs such as Industrial Foundation Class (IFC). The HFM has a one-to-one correspondence with the component structure of existing or planned HVAC systems. HFM nodes such as spirals, channels or fans model the dynamic physical behavior (temperature, flow, humidity and pressure) of air and water flows from these components as accurately as can be derived from BIMs. Each node can calculate its physical behavior parameter values upstream and downstream of the HVAC components connected with the dynamic output data from the HVAC control system using sensor values and instrument control. The results are propagated through the HFM graph as parameter variations.
    [0013] The modalities apply failure rules to each HFM node. If the variations in the calculated and received evaluated values do not match, a defect is assumed and its severity is propagated in the component hierarchy for diagnosis. The diagnosis is performed by a central engine by mapping the rule violations of the HFM nodes to the failures of the HVAC systems, the mapping relationship is represented by an Associative Network. The FDD-based HFM is comprised of an engineering tool and runtime system.
    [0014] The modalities create a model based on HFM graph to build HVAC FDD system. The modalities automatically extract the necessary structural and quantitative data about the target system from the BIM descriptions, for example, the IFC Rules for detecting defects that are related to the graph's nodes are defined based on first principles.
    [0015] One aspect of the invention provides a Thermal Flow Model (HFM) node used in a Heating, Ventilation and Air Conditioning (HVAC) defect detection chart. Aspects according to the HFM node include an Fwdln end configured to receive parameter variations from a downstream direction, an FwdOut end configured to output parameter variations in a downstream direction, a Revln end configured to receive parameter variations from a downstream direction. upstream direction, a RevOut end configured for output parameter variations in the counter current direction, and node specific configuration data that define node functionality.
    [0016] Another aspect of the HFM node is one or more RuOutOut ends configured to transfer a defect rule decision where the node specific data also comprises a defect rule corresponding to each RuIesOut end where, for the downstream direction, a failure rule compares an evaluated parameter variation with an end parameter variation Fwdln and for the upstream direction, a failure rule compares an evaluated parameter variation with an end parameter variation Revin, and if the parameter variation evaluated is not within the parameter variations received, a failure is produced.
    [0017] Another aspect of the HFM node is that the node-specific configuration data also comprises Fwdln end parameter variation tolerances and Revln end parameter variation tolerances.
    [0018] Another aspect of the HFM node is for the downstream direction, an evaluated parameter variation is a product of an end parameter variation Revln and an end parameter variation tolerance Revln, and for the upstream direction, an evaluated parameter variation is a product of an Fwdln end parameter variation and an Fwdln end parameter variation tolerance.
    [0019] Another aspect of the HFM node is one or more endpoints, each configured to receive a HVAC control system variable, and the node-specific configuration data also comprises HVAC control system variable parameter tolerance values. dynamic endpoint Dataln.
    [0020] Another aspect of the HFM node is for the downstream and counter current directions, an evaluated parameter variation is a product of a Dataln end dynamic HVAC control system variable and its control system variable parameter tolerance value. Dynamic HVAC.
    [0021] Another aspect of the invention is a method of using a Thermal Flow Model (HFM) graph to detect defects in the HVAC system for a construction. Aspects according to the method include translation of formal HVAC system descriptions from a Building Information Model (BIM) for construction as HFM nodes, retrieving BIM HVAC component attributes for each HFM node, retrieving predefined HFM nodes an HFM node library, creating connectivity between different HFM nodes from the BIM connectivity data, compiling HFM nodes in an HFM chart, inserting real-time data from the construction of the HVAC control system to the HFM chart for defect detection, detection of defects in building HVAC systems using rules defined based on the first principles that are related to HFM nodes, and mapping of rule violations of HFM nodes to the construction failures of the HVAC control system.
    [0022] Another aspect of the method is where each node in the HFM graph evaluates the values of physical behavior upstream and downstream that correspond to its construction HVAC components with dynamic output instrument sensor and control data from the HVAC control system of construction and propagates the values of physical behavior upstream and downstream through the HFM graph as parameter variations.
    [0023] Another aspect of the invention is a defect detection system (HVAC) for ventilation and Air Conditioning, for heating construction. Aspects according to the method include an interface configured to access a Building Information Model (BIM) file library and import the BIM files from the building HVAC system, such as an HFM node library configured to store a plurality of types of predefined different HFM nodes where an HFM node models the parameters of dynamic physical behavior of air and water flows in HVAC components as derived from the BIM files, a Graphical User Interface (GUI) configured to insert and edit HFM nodes and configuration data from connection during an HFM graph assembly, a compiler coupled to the interface and GUI, configured to compose together with the BIM file data with the additional configuration data, and generated from Defect Detection and Diagnosis (FDD) coupled to the compiler and HFM node library, configured to compare the BIM file types with the build HVAC system with the predefined HFM node types and select node s HFM that correspond to and generate an HFM graph where the HFM graph is by mass airflow path corresponding to the components and behavior of the construction HVAC system.
    [0024] Another aspect of the system is an FDD engine configured to justify the HFM graph as an execution phase system for the construction HVAC control system, and an interface configured to access the construction HVAC control system, where the engine FDD runs the HFM chart with the HVAC control system process and variable control data and applies rules to detect defects in the HVAC system.
    [0025] Details of one or more embodiments of the invention are shown in the attached drawings and description below. Other features, objects, and advantages of the invention will be clear from the description and drawings. Brief Description of Drawings
    [0026] Figure 1 is an exemplary HVAC Fault Detection and Diagnosis (FDD) integration.
    [0027] Figure 2 is an exemplary air handling unit (AHU) channel and an instrumentation diagram.
    [0028] Figure 3 is an example graph of the Thermal Flow Model (HFM).
    [0029] Figure 4 is an example HFM chart for the AHU illustrated in figure 2.
    [0030] Figure 5 is an exemplary downstream temperature propagation for the HFM AHU graph illustrated in Figure 4.
    [0031] Figure 6 is an exemplary failure rule situation illustrating five different calculated evaluation tolerance variations.
    [0032] Figure 7A is an exemplary HFM sensor channel node.
    [0033] Figure 7B is an exemplary HFM temperature sensor channel node.
    [0034] Figure 7C is an exemplary HFM flow controlled fan node.
    [0035] Figure 7D is an exemplary HFM pressure controlled fan knot.
    [0036] Figure 7E is an exemplary HFM coil knot.
    [0037] Figure 7F is an exemplary HFM thermostat knot.
    [0038] Figure 7G is an exemplary HFM mixing box knot.
    [0039] Figure 7H is an exemplary HFM two-way branch node.
    [0040] Figure 7I is a Variable Air Volume (VAV) node for HFM reheating.
    [0041] Figure 8 is an exemplary HFM nodal analysis of FDD system.
    [0042] Figure 9 is an example table illustrating types of Industrial Foundation Class (IFC) corresponding to the HFM nodes. Detailed Description
    [0043] The modalities of the invention will be described with reference to the figures in the accompanying drawings in which similar numbers represent similar elements everywhere. Before the modalities of the invention are explained in detail, it should be understood that the invention is not limited in its application to the details of the examples demonstrated in the description that follows or illustrated in the figures. The invention is capable of other modalities and can be practiced or carried out in various applications and in various ways. Also, it should be understood that the phraseology and terminology used here is for descriptive purposes and should not be considered as limiting. The use of "including", "comprising", or "having", and their variations should be intended to include the items listed below and their equivalents, as well as additional items.
    [0044] The terms "connected" and "coupled" are used widely and include both direct and indirect connection and coupling. In addition, “connected” and “coupled” are not restricted to physical or mechanical connections or couplings.
    [0045] It should be noted that the invention is not limited to any particular software language described or that is implied in the figures. One skilled in the art understands that various software languages can be used to implement the invention. It should also be noted that some items and components are illustrated and described as if they were hardware elements, as is customary in the art. However, one skilled in the art, and based on a reading of this detailed description, would understand that, at least in one modality, the components in the method and system can be implemented in software or hardware.
    [0046] The modalities of the invention provide methods, system structures, and a usable computer medium that stores computer-readable instructions that translate formal HVAC system descriptions of a BIM into a Thermal Flow Model (HFM) chart for HVAC FDD matching one-to-one with the component structure of a building HVAC system, which are compiled into an executable FDD system. The real-time data from the construction HVAC control system is entered into the FDD system for fault detection and diagnosis. The invention can be developed as software as a tangible application program embedded in a program storage device. The application code for execution may reside in a plurality of different types of computer-readable media known to those skilled in the art.
    [0047] An HFM is a graph understood by HFM nodes that correspond to the real components of the HVAC system. HFM nodes correspond to mass flow connections such as channels or pipes or electrical energy. HFM nodes simulate dynamic functionality (behavior) of HVAC components as precisely as the supplied BIM parameters and dynamic control system data allow. An HFM chart can be generalized as a component hierarchy. This allows for different levels of refinement and abstraction.
    [0048] A BIM is a digital representation of the physical and functional characteristics of an installation and serves as a resource of shared knowledge for information about an installation. One BIM is the Industry Foundation Casses (IFC). The IFC modeling language is powerful and can be extended to describe every detail of an HVAC system. However, there is only one way to model a specific system. The modalities use IFC models of existing or planned constructions to create FDD systems automatically.
    [0049] IFC is one of the most commonly used formats for interoperability. The IFC model provides a standard representation of underlying objects, their properties and relationships including HVAC, electrical, plumbing, fire protection, construction control, etc.
    [0050] An HFM based on the FDD system can be integrated into an existing construction HVAC supervisory control system structure. The FDD system can communicate directly with the top level control. However, an HFM graphical representation allows a high degree of modularity of the FDD system and integration at any level of the system as a distributed system.
    [0051] Figure 1 illustrates an exemplary HVAC control system 1. System 101 comprises a single channel zone Air Handling Unit (AHU) 103 to supply hot or cold air to two Variable Air Volume (VAV) systems ) of reheating 1051, 1052 (105 collectively). Each Variable Air Volume (VAV) 105 distributes hot or cold air to a space via outlets with thermostatic feedback control. The return air is collected and returned to AHU 103.
    [0052] Figure 2 illustrates the structure and control of the AHU 103. The AHU 103 comprises an Rfan return air fan, an Sfan air supply fan, a Mixer mixing box, a Hcoil heating coil with control valve. Hcv modulation, a Ccoil cooling coil with Ccv modulation control valve, four Tra, Toa, Tma, Tsa temperature sensors and a Psa pressure sensor. The Sfan air supply fan can be pressure controlled. The Rfan return air fan is controlled by a return air pressure to maintain the balance of air flows. The pressure controlled fan can include a differential pressure sensor to measure airflow, which is monitored by an AHU controller (not shown). A Variable Air Volume (VAV) 105 typically comprises a damper to control airflow into a space, a heating coil, a distribution temperature sensor, and an air flow rate sensor (not shown).
    [0053] An HFM comprises two types of data: 1) mass comprising air and water, and 2) electrical energy, and is represented by a graph with nodes endowed with parallel and anti-parallel ends. The HVAC components of the HFM node model and each node end insert or transfer state variables and information flows.
    [0054] Figure 3 illustrates the parallel and non-parallel end interconnections of the HFM nodes 1 and 2. The physical parameter data of parallel Fwdln end inputs for the node of a downstream direction and physical parameter data of the end outputs FwdOut which can be the same or different in the downstream direction of the mass flow. The physical parameter data of Revln endpoints for the node of an upstream direction and the physical parameter data of RevOut endpoints that can be the same or different in the upstream direction. A heating coil node can have separate Fwdln / FwdOut ends and Revln / RevOut ends for interconnecting physical data of heating water and air if the air and water are modeled.
    [0055] The number of Dataln antiparallel ends and RuIesOut ends vary depending on the number of mass or energy flows that are considered in a node. Each Dataln end inserts a dynamic HVAC control system sensor (process variable) and control data (control variable). Each RuIesOut end transfers a specific HFM node failure rule value to an FDD engine.
    [0056] For each HFM node, the actual physical data is represented as a vector. For FDD, the air flow data parameters of interest are temperature, mass flow rate, humidity and pressure.
    [0057] To generalize HFM node interconnections, a vector is propagated with four parameter variations of the air flow data between the HFM nodes in the downstream and Aj and counter current directions Bj,

    [0058] The vectors downstream Aj and upstream Bj comprise the parameter variations 1) Tmin, Tmax which are the minimum and maximum dry bulb air temperatures, 2) Qmin, Qmax which are the mass air flow rates minimum and maximum 3) Hmin, Hmax which are the minimum and maximum water vapor pressures, and 4) pmin, pmax which are the maximum and minimum air pressures.
    [0059] The heating or cooling water vector variable state data comprises temperature, flow and pressure values.
    [0060] Figure 4 illustrates an HFM 401 node chart for AHU 103 comprising eight HFM nodes that include five types of HFM nodes: two fan nodes (Rfan, Sfan), two sensor channel nodes (Rduct, Mduct) , a mixer node (Mixer), two coil nodes (Hcoil, Ccoil) and a two channel sensor node (Sduct). The HFM 401 chart is illustrated without the antiparallel Dataln / RuIesOut ends.
    [0061] The downstream flow starts with the return supply fan Rfan node. Rfan is coupled to a channel segment Rduct node that includes a Dataln end for temperature sensor Tra data: Rduct is coupled to a mixer box mixer node that includes a Dataln end for external temperature sensor data. The mixer is coupled to a channel segment Mduct node that includes a Dataln end for mixing temperature sensor Tma data. Mduct is coupled to two successive Hcoil and Coil coil nodes to heat and cool. Ccoil is coupled to a channel segment Sduct node that includes a Dataln end for temperature sensor Tsa data to indicate AHU outlet temperatures and a Dataln end for pressure sensor Psa data to indicate AHU outlet pressure .
    [0062] AHU 103 can be modeled as an HFM node at a higher level in the hierarchy with air and water inlets, if the heated water supply system is modeled.
    [0063] Each HFM node performs two functions: 1) a downstream FwdOut vector vector calculation and upstream RevOut vector β vector state variables which it transfers to adjacent nodes based on the assessments derived from the data of HVAC control system (Dataln) and a node configuration, and 2) a calculation of one or more failure rules (RuIesOut) as applied to the Fwdln Aj end vector state variable parameter variations and upstream Revln end vector Bj based on the evaluated parameter variations derived from the control system data (Dataln) and the node configuration.
    [0064] Each HFM node receives a downstream vector Aj and an upstream vector Bj. Depending on a particular node's functionality (behavior), one or more downstream vector state variables Aj and one or more upstream vector Bj can change. The changing state variables are reflected in the output of the vector state variables downstream and against current õ> + 1 by that node. The variables of R vector state downstream> * 'and upstream are propagated to the adjacent HFM nodes (figure 4).
    [0065] Each HFM node performs an evaluation calculation. Evaluations are different from construction performance simulators in that they lack detailed information about the components of the supervised system, the behavior of the control system and accurate sensor data. Due to the lack of detailed information, an HFM node cannot perform dynamic calculations in their entirety. However, as HVAC control systems react slowly to environmental disturbances, steady state behavior is considered.
    [0066] What is not neglected are the frequent rule activations due to fluctuations in the control system or because the excess of control actions can reduce the useful life of the components. It is the fault diagnosis task to distinguish between different types of short-term rule activation.
    [0067] The node rule evaluations are based on the vector state variables downstream Aj and upstream Bj, on the effect that the node has on one or more vector state variables, and on the sensor / control data entered from the HVAC control system during the execution phase.
    [0068] For the following example, only the variation of the temperature parameter Tmin Tmax of the downstream vector Aj is considered. The Hcoil node receives the vector As having a temperature measured from two HFM nodes upstream in the Mixer by the temperature sensor Tma. Hcoil receives the HVAC control system (Dataln) that modulates its Hcv heating coil inlet control valve. The maximum heating energy of the BIM is stored as a specific Hcoil node configuration parameter.
    [0069] Figure 2 illustrates the evaluation problem in which there are many uncertainties, starting with the tolerance of the air temperature sensor Tma and the distribution of unknown air temperature through the channel in which the sensor is mounted. The temperature of the heated water is not known nor is the flow rate of the heating water. It can be assumed that maximum heating energy can be achieved if the heating water inlet valve is 100% open and the water pressure and temperature are at maximum design values. Therefore, the air outlet temperature of the Hcoil node can be better assessed when the valve is closed, or a wide range of possible values must be assumed.
    [0070] For this reason, each node receives a downstream vector Aj and an upstream vector Bj. Each upstream and downstream vector parameter is expressed as a minimum and a maximum of an evaluated parameter variation. If a normal Gaussian distribution is used for the density probability function of values expected to be in the range, a standard deviation ± from the mean value is used as the range limits. In most cases, the format of the distribution is not known and the assumed limits of variation define the variation.
    [0071] The first step in deriving a node failure rule (s) is to list possible and probable defects. For example, outputs from temperature and pressure sensors may deviate or fail by a value. The valves and dampers can leak or fail in their last position. Filters and pipes can clog. Process controllers may malfunction. Design defects are also possible. For example, the cooling coil can be very small and cannot compensate for an expected thermal load.
    [0072] Any of these defects can cause several failures that can be detected internally or externally. External faults can be measured directly or felt by occupants. For example, a space temperature deviation from a controller setpoint. Internal faults refer to situations where the control system compensates for a defect. For example, a leakage from the heating coil valve is compensated by the increased cooling of the control system. The user is not affected, but energy consumption increases. The detection of internal failures is also important due to the propagation of failure. The closer a fault is to a fault, the easier it is to find it.
    [0073] A failure rule is a conditional inequality, rule = (condition exprl <expr2 - limit), (3)
    [0074] Where exprl is a parameter variation of minimum vector state variables, expr2 is an evaluated maximum of parameter variation and limit is a tolerance. If a rule output is true, there is a defect. Conditions are premises for control values.
    [0075] AHU 201 has mixed air temperature Tma and provides sensor measurements of air temperature control system Tsa. The Hcoil node includes an Hcv modulation control valve and has a Dataln lthc design control input. The Ccoil node includes a CCv modulation control valve and has a Dataln ucc end control input. the built-in HVAC control system transfers a control variable in a range from 0 to 1 to modulate control valves. rule - u.c = 0 and ucc-0: Tsa <Tma-J <4)
    [0076] Rulel (Rule 1) explains that if neither heating nor cooling is provided = ° w «~ °) • the supply temperature Tsa must not be lower than the mixed air temperature Tma, with the combination of a tolerance of mixed air temperature sensor Tma and the Sfan power fan thermal load expressed as a £> limit. If rule 1 is true, the CCv cooling coil valve may leak and / or temperature sensors Tma and / or Tsa and may have experienced a defect. If the Hcv cooling coil valve is leaking, the roller would be true The example shows that several defects can trigger the same rule. The opposite is also true: a defect can trigger several rules.
    [0077] The example of the downstream direction temperature rule illustrates that in addition to the control system inputs and for modulating control valves Hcv and Ccv, sensor values Tma and Tsa are required from the Mduct and Sduct nodes. The Mduct and Sduct nodes are separated by the Hcoil, Ccoil and Sfan nodes. For modularity, the wheel of (4) must be evaluated in one of the five nodes. None of the five node types have Tma or Tsa temperature sensor values.
    [0078] The solution to the need for temperature sensor values for both Tma and Tsa is downstream and backstream propagation of sensor data via the parameter variations of downstream vector state variables Aj and upstream Bj.
    [0079] Instead of a direct propagation, which results in several values propagated in parallel, the data is transformed by the nodes according to their physical behavior.
    [0080] For example, the Hcoil node receives temperature measurement Tma from the upstream node Mduct. The Hcoil node increases Tma according to the control variable and creates a THC heating coil temperature. The Hcoil node propagates THC downstream to the Ccoil node. The Ccoil node receives temperature The and decreases The according to the control variable and creates a cooling coil temperature Tcc. The Ccoil node propagates Tcc downstream to the Sfan node. The Sfan node receives temperature Tcc and increases Tcc by a predetermined amount due to the thermal load of the fan motor and creates a supply fan temperature Tsf. The Sfan node propagates Tsf downstream to the Sduct node. The Sduct node receives Tsf temperature and applies the roller in a modified form using only local variables such as

    [0081] The limit has changed because the thermal load of the Sfan node is considered at the Tsf temperature. In principle, rulel (5) can be generalized because inequality signals a defect for any combination of control variables e, fcí '. This results in rulel = [TSÍJ <Tsf-ε ^) ■ (6)
    [0082] Because the propagation also works upstream, the rules equivalent to (5) and (6) can be evaluated on each of the five nodes. A failure decision depends on several general principles.
    [0083] A principle is that a rule must be applied to the node where a defect can trigger it. As this relationship is one to one, the same rule can be evaluated on several nodes. For rulel (5), the applicable nodes would be the Mduct, Ccoil and Sduct nodes because a defect in any one would trigger the same rule in all three nodes. This redundancy must be solved by the FDD that runs on a node at the top of the hierarchy. The advantage of this concept is that nodes can be easily typed and reused for implementations.
    [0084] Another principle is to evaluate a rule at the node in the flow that provides the value of a precise state variable, typically a sensor value. In the example, this is the Sduct node that receives Tsa temperature sensor data.
    [0085] The problem of the variable values of the vector state parameter is inherent tolerance. For example, in (4) the tolerance is observed by the limit ε •. The limit value ε ■ depends on the effect of several nodes. This decreases the modularity and reuse of node models.
    [0086] To overcome these problems, the modalities calculate the variations of the variable parameter of the state of the downstream vector Aj and upstream Bj within each node, and propagate the variations of the variable parameter of the state of the calculated downstream vector and of the vector a amount for an adjacent node. A node rule tolerance is a function of the control system Dataln end values received during the execution phase and variable configuration parameters for the node obtained from the supervised system's design data. Tolerance is also the result of evaluation uncertainties.
    [0087] For example, the Mduct node Tma temperature sensor value can have a tolerance of ± 0.5 ° C. During the execution phase. 21 ° C is measured and inserted (Dataln) for Mduct. The Mduct node propagates the variable temperature variation of the state of the vector Tmin, Tmax as 7wo = 2l.0oC ± 0.5 °, (7)

    [0088] for the Hcoil node and propagates the temperature variation of R - vector state * Tmin, Tmax as

    [0089] for the Mixer node.
    [0090] The Hcoil node has configured values, predefined for the maximum air temperature increases in the maximum mass air flow rate and maximum heating water temperature, each parameter having a tolerance. Since the mass air flow rate was not measured by the HVAC control system, it can be assumed that the increase in the maximum air temperature TincrMax -20.0 ° C is independent of the air flow rate. During the execution phase, the Hcoil Hcv node control valve can receive a control variable = O-5 (Dataln). If the increase in the heating coil air temperature is proportional to the heating water flow rate, the control variable indicates a heating water flow between 40% and 60% considering the non-linearity of the valve. Therefore, the TincrMax assessment of increasing the maximum air temperature of the Hcoil node is

    [0091] Using (10), the ThcMino minimum air temperature rise rating of the Hcoil node is calculated as 28.5 ° C. This is a local calculation on a node with no knowledge of other nodes. The Hcoil node propagates the variable temperature variation Tmin, Tmax of the vector state AΘ

    [0092] Figure 5 illustrates a propagation downstream of the variable temperature variation Tmin, Tmax in the Mduct node and passing through Hcoil, Ccoil and Sfan and ending in Sduct.
    [0093] The resulting large tolerances render the state variables useless. This allows the use of the more general rule2 (6) instead of the roller (5). For the case of closed Hcv heating valve ~ ° 'the tolerance decreases for that of the temperature sensor Tma and if the CCv cooling valve is closed u'c ~ θ' (6) includes the case (4).
    [0094] Using the variable representation of the vector state variable parameter, the fault rules can be generalized. This is an important contribution to the reuse of the node. For example, Δ the Sduct node receives the vector * having a temperature variation Tmin, Tmax and Dataln Tsa end temperature sensor value that is transformed to a temperature tolerance variation.
    [0095] As a first approximation it can be assumed that no defects can be deduced if the variable temperature variation of the vector state and the temperature tolerance variation Tsa overlap, because there is a probability that the actual air temperature is within the union of both variations. A defect is assumed if the variable temperature variation of the vector state and the temperature tolerance variation Tsa do not overlap.
    [0096] Figure 6 illustrates five situations for the Sduct node that receives the vector "having a temperature variation Tmin, Tmax and comparing it with five different temperature variations of calculated evaluation for temperature sensor Tsa. Cases 1 and 5 they are for two different fault rule activations, case 3 is within the limits and, therefore, no defect, and cases 2 and 4 have a probability of representing a defect.
    [0097] As a refinement, a continuous failure value can be calculated in addition to the result being true or false. In its simplest form the interval between a parameter variation, and its corresponding calculated evaluation parameter variation can be taken as a measure of the size of the failure if true. Figure 6 illustrates two error calculations illustrated as double arrows for cases 1 and 5. The reason is if a rule is evaluated as true, there is still a probability that no failure has occurred because the tolerance variation or a limit may have been chosen too small. The higher the rule's value, the lower the probability, or expressed differently, the greater the probability of a failure. Therefore, this rule value can be used for failure analysis in addition to the true and false values.
    [0098] The following equations represent the solution for the example above. rule3 = max (O, TsaMin - Fwdln Tmax) and rule4 -max (0, Fwdln Tmin - TsaMax), (15)
    [0099] where TsaMinθ is a minimum evaluated evaluation and T * aMax is a maximum evaluated variation. As long as there is an overlap, rule3 and rule4 are zero. For case 1, rile3> 0. For case 5, rule4> 0. Cases 1 and 5 indicate failures.
    [00100] A more general solution is the relationship between two temperature variations T1 and T2. ru / e l = max (0. T2 4m-T ^ iax), and rule2 = max (0, TtMin - T ^ Max). (17)
    [00101] An extension to (16) and (17) would be normalization. (16) and (17) can also be applied to another temperature variation and to check if the temperature control is working properly. The temperature is used as an example. Any variable state parameter can be used as rules for checking for faults.
    [00102] The above illustrates that the nodes of the HFM graph can perform estimation and evaluation without knowing anything about the rest of the graph besides knowing its adjacent nodes for the communication of the flow vectors. This is the case for object orientation with information hiding. For the purpose of generating distributed systems, multi-agent technologies can also be applied without problems.
    [00103] Not all problems can be solved locally at the nodes at the granularity level that was shown. This is generally not a problem, as a graph is hierarchical and composite nodes are possible as AHU 401 in figure 4. AHU 401 is a composite HFM node.
    [00104] There are also nodes that are not part of the flow such as a controlling node. Controllers evaluate different types of rules. For example, if the variables of a heating and cooling coil control valve control are greater than zero at the same time, one or both controllers sustained a defect.
    [00105] The advantage of goal orientation is the use of types and the derivation of subtypes by succession. A type can also produce many occurrences in the execution phase. For the purpose of automatically generating an executable FDD system from a BIM, a library of HFM node types must be provided. The goal would be to keep this library small to reduce software design and maintenance costs.
    [00106] The AHU 401 unit illustrates five different types of nodes. Examples of knots are air flow inlet and outlet nodes. The Mixer node does not model flow interfaces for the external environment. The Hcoil and Ccoil nodes do not model their heating and cooling water flows. Such simplifications are appropriate if nothing is known about missing parts. However, the total modeling of the system improves rule estimation and evaluation.
    [00107] Figure 1 illustrates that AHU 103 serves several VAV 105 zones. This requires “Branch” nodes with one airflow inlet and several outlets and “Joint” nodes with several air inlets. flow and an outlet. VAVs 105 are modeled as complex nodes with several encapsulated components. The number of VAV types remains small enough for a node library.
    [00108] Figure 7A illustrates a sensor channel node that includes Dataln Xsens and XSet ends. XSet is a controller setpoint and Xsens is a sensor variable. Xsens is controlled externally to be within a range of XSet. Rule3 (14) tests whether this is the case. The toll and tol2 estimators transform the Dataln Xsens and XSet inputs into variations in the XSens and XSet tolerance configuration. Rulel (16) rule2 (17) and rule3 (14) apply.
    [00109] The sensor channel node is generated and can be applied to the temperature, flow, humidity and pressure sensor situations. One or more sensors can be modeled on the same channel, representing controlled and uncontrolled variables. The same type of node can also be modified for water flow nodes. Figure 7A does not illustrate flow connections from inputs directly to outputs for variables that are not perceived. These variables do not change in short channel sections and are propagated directly.
    [00110] Figure 7b illustrates a temperature sensor channel node that includes Dataln end Tsens. The Tsens temperature tolerance range is Tsens Max = Tsens + tolerance, and TsensMin = Tsens -tolerance. (19)
    [00111] Rulel (16) and rule2 (17) compare the variation of tolerance Tsens Tsens A / αr with the variations Tmin, Tmax of the vector Aj and Bj.
    [00112] Figure 7C illustrates a flow-controlled fan node that includes the Q Dataln end (flow sensor measurement). A fan knot is an estimation function. The air temperature typically increases on the order of 1 ° C from Fwdln to FwdOut due to the thermal load. Since the increase includes a tolerance, the temperature variation T. TFwdOut.4 ^. min texture, max Jexpande and is wider than the temperature variation Tmin, Tmax Fwdln AjIn the upstream direction, the temperature variation Tmm, Tmaxdiminui of RevIn for RevOut β- (Jfor the same amount.
    [00113] If the fan is a constant flow rate type, it can propagate the flow rate downstream and upstream. Figure 7D illustrates a pressure controlled fan node that includes P Dataln end (pressure sensor measurement). For fans of a constant pressure type, the pressure can be propagated downstream, but not upstream. Pressure-controlled fans vary the air flow rate. Therefore, the air temperature increases (temperature change. -T FwdOut .4., 'Mm, tmax J) changes as a function of the rate. Modern windmills reduce electrical energy proportional to the rate of air flow and thus keep the temperature rise constant.
    [00114] The temperature variations propagated from the controlled flow fan node: FwdOut Tmax = Fwdln Tmax + dTfmax, (20) where - maximun Jan heat load, RevOut TKÍax - RevlnTMax - dTfmin, dTfmin - minimum fan heat load, where FwdOutTmin = FwdlnTmin + dTfmin, and RevOutTmin = RevInTmin-dTfmax. Variations of propagated flow: FwdOut Qmax - Qmax RevOut Qmax = Qmax, (25) FwdOut Qmin ~ Qmin, (26) RevOut Qmin - Qmin. (27)
    [00115] Rulel (16) r rule2 (17) compares estimated temperature and flow with propagated values. The propagated temperature variation (figure 7D) of the pressure controlled fan node: FwdOut Tmax = Fwdln Tmax + dTfmax, (28), dTfmax - maximun fan heat load t where JJ RevOut Tmax = RevInTmax - dTfmin, (29) dTfmin = minimum fan heal load, where FwdOut Tmin = Fwdln Tmin + dTfmin, (30) RevOutTmin = RevinTmin-dTfmax. (31)
    [00116] Propagated pressure variation: FwdOut Pmax = Pmax r RevOut Pmax = Pmax, FwdOut Pmin = Pmin, and RevOut Pmin - Pmin.
    [00117] Figure 7E illustrates a coil knot that includes Dataln ctrlin end. The coil knots for heating or cooling are equipped with complex physical models. The number of parameters and unknown state variables is large. Therefore, it is necessary to create several typical coil node types in the library or one type with different selectable estimators.
    [00118] Figure 7F illustrates a thermostat node that includes Dataln Tsens and TSet ends. TSet is the set point of the thermostat and Tsens is the variable of the temperature measurement process. The temperature variation propagated is FwdOul Tmax = Tsens + tolerance t and FwdOut Tmin - Tsens - tolerance. . (37)
    [00119] Rulel (16) and rule2 (17) compares a variation in node temperature tolerance with a propagated temperature variation (Fwdln, Revln). Rule3 (14) compares a setpoint Tset assessment with an estimated temperature measurement.
    [00120] Figure 7G illustrates a mixing box knot that includes the Dataln Outdoor Tsens / Damper Ctrls end. A physical mixing box includes a Toa temperature sensor to measure external air and to change the supply air temperature measured by another temperature sensor Tma by the external mixing air with return air measured by another temperature sensor Tra. The mixing ratio is controlled by three modulation buffers (figure 2). The supply flow rate equals the return flow rate if no outside air is admitted. But it cannot be assumed that the mixing rate is proportional to the damping control signal of an AHU controller. There are two extremes when the external air damper is either fully closed or fully open that the rate of mixing is precisely known. The largest deviation due to non-linearity can be assumed to be 50% open. This relationship is considered in the mixing function when evaluating the temperature of mixed air and humidity.
    [00121] The temperature variation propagated is FwdOut Tmax = Tsens + tolerance, / oox (3θ) and FwdOut Tmin = Tsens - tolerance. (39}
    [00122] Est2 estimate for temperature variation T for Tma using damper opening percentage and Toa estimate.
    [00123] Rulel (16) and rule2 (17) are comparisons of the variation in temperature tolerance with propagated temperature variation. Rules (14) is a comparison of estimated Tma using damper openings and thermal dynamics for the measurement of estimated Tma.
    [00124] Figure 7H illustrates a two-way branch node. A channel branch propagates air temperature, flow, pressure and humidity, and allows for the opposite calculation of air flow rate. The air flow rate is measured in all VAVs. The sum of all measured values is the air flow rate at the branch inlet as a RevOut value.
    [00125] Estimate for temperature and flow variation: FwdOut1 Tmax = FwdlnTmax + dTfmax, (4 0) where dTfmax - change through branch t RevOut Tmax = Max (Revln Tmax - dTfmin, Revin! Tmax - dTfmin}, (41) where DTfmin = change through branch, FwdOuti Tmin - Fwd / n Tmin + dTfmin, (42) RevOut Tmin = Min (Rev / n Tmin -dTfmax, Revln2 Tmin - dTfmax) FwdOitllTmax = FwdlnTmax + dTfmax, (44) and FwdOut2 Fwdfn Tmin + dTfmin. (45)
    [00126] Rulel (16), rule2 (17) and rule3 (14) compare temperature tolerance variation with propagated temperature variation.
    [00127] Figure 7I illustrates a reheating VAV node that includes Xsens and XSet ends Dataln. XSet is a controller setpoint and Xsens is a sensor variable. A reheat VAV is a complex node. If equipped with sensors for air flow rate and distribution temperature, an air flow rate can be assessed using the Fwdln tip propagated pressure variation and the damper control value. Using the inlet air temperature, the measured flow and, for example, the electrical energy value of the reheat coil, the distribution temperature can be evaluated. Both estimates can be evaluated with the sensor values in the fault rules using (16) and (17). In addition, if a setpoint is defined, compliance can also be assessed.
    [00128] The estimate for temperature variation is FwdOut Tmax = Tsens + tolerance, and FwdOut Tmin = Tsens - tolerance. (47)
    [00129] Rulel (16) and rule2 (17) compare estimated temperature variations with propagated temperature variations. Rule3 (14) compares the estimated Tset setpoint with estimated Tsens measurements.
    [00130] The space that is served by heating or cooling a VAV and has a temperature sensor to control the VAV can propagate downstream and upstream variations. At a minimum, you can assess whether the air temperature is at the set point. If the thermal load and the loss in space are known, especially in closed environments, the estimates of the required heating and cooling energy can be compared with VAV distributed air parameters as a failure rule. This may require the outside temperature as a data entry.
    [00131] The return air from the spaces is calculated in a union channel. Failure rules can be applied if the air temperature can be estimated. The air temperature in each space is known with tolerances at the sensor site to control VAVs. At the air inlets the air temperature will be different, adding to the tolerance. If the air flow rate of the return air from each space is known, temperature variations for the united air can be calculated. In open plan offices, individual flow rates are not known. The upper limit of the variation cannot be higher than the highest measured sensor value, with the lower limit not being lower than the lowest. The FwdOut end temperature range can be used to detect, for example, a major defect in the return air sensor.
    [00132] The HFM graphs specify the physical structures and defect detection functions. The modalities specify a hierarchical structure of autonomous communication processes that can be interpreted as agents. HFM nodes can be mapped directly, for example, in Specification Description Language (SDL) processes. The advantages of SDL models are that a model can be automatically translated into C code and compiled into executable prototype systems. The prototype SDL experiments illustrate that the defects introduced lead to positive failure rule outputs that are collected in files.
    [00133] The modalities comprise an engineering phase and an execution phase. Figure 8 illustrates an HFM 801 build FDD system. The HFM 801 build FDD system comprises an 803 interface configured to access and import BIM / IFC files, a 805 Graphical User Interface (GUI) configured to allow for the insertion of additional data. during graphical assembly and visualization of connection information between different components and entering and configuring missing parameters, conditions, and switches not found in BIM, and an 807 compiler to compose together the file data with additional engineering information.
    [00134] The benefit of HFM is the engineering efficiency brought to HFM based on FDD systems. Due to the modularity of HEM, it can be automatically composed of BIM and graphic models can be effectively compiled into executable FDD systems. IFC can be used to derive an HFM model since IFC is one of the most commonly used BIM formats.
    [00135] IFC provides a set of definitions for all types of object elements found in the HVAC control and mechanical system and a text-based structure for storing those definitions in a data file. It comprises two main parts: ifcElement QifcPort.um and | emenf0 p0C | and be any component that can be connected to adjacent elements, using one or more ports. IFC elements include flow segments (channel), flow settings (channel union), mobile devices (fans), flow controllers, etc. Each HFM node can be mapped from the IFC elements.
    [00136] IFC element objects have defined the basic properties and attributes that are used for HFM node graphics. Figure 9 illustrates a list of the mapping relationship between the HVAC IFC elements and the HFM node types. The succession of an HVAC node type is accomplished by defining the associated IFC type of each element. There is no direct mapping for node types such as: Mixer, Reheat VAV and they are composed based on IFC elements.
    [00137] The connectivity model for HFM can be derived through IfcPort. “Port” is a point on the network where the elements are connected to each other. An IfcPort is associated with an IfcElement, to which it belongs, through the objectified relationship
    [00138] ifcRelConnectsPortToElement.Therefore, the connection information for HFM can be obtained through the IFC search model for IFC element objects, IFC port objects, and ifcRelConnectsPortToElement objects. Figure 9 illustrates the correspondence between IFC types and HFM nodes.
    [00139] HFM reduces the engineering effort required to configure FDD systems for different HVAC systems. HFM is the bridge between BIM and the compiled graphics model based on executable FDD systems.
    [00140] The 807 compiler is coupled to an HFM 809 engine that is configured to generate an HFM model in an Extensible Markup Language (XML) format, the HFM model is inserted into an FDD 811 generator configured to populate the HFM node types identified with functional HFM graphical nodes from an HFM 813 node library.
    [00141] The off-line derived HFM graph can be loaded into an execution phase FDD system to justify an FDD engine for a specific construction HVAC control system and configured to run the HFM graph by introducing HVAC control system process and control variables of an 817 input interface. During the execution phase, the data from the real-time HVAC control system is inserted into an FDD 819 engine configured to analyze the rules used in the HFM graph.
    [00142] HFM makes defect detection for the HVAC system realistic. To save effort to configure FDD-based HFM for each specific construction, object orientation is used for modeling as well as for implementation. The objects in the model represent real components with functions to capture the correct behavior and the rules for detecting defects. The object models are provided in a node library and are assembled as graphs per signal path as real components are connected by channels and pipes. Objects can be composed and decomposed hierarchically.
    [00143] The HFM can have many different types of knots. Using succession, more types of nodes do not mean more effort to extend the library.
    [00144] If the FDD is hosted in the center by a construction management system or distributed in digital process controllers, it receives execution phase control signals and sensor data from the respective HVAC control system. For each HVAC node, based on the measurements and flow information received from upstream and downstream nodes, the HFM estimates the state, propagates the resulting vector parameter variations to adjacent nodes, and also detects defects. Defects are also propagated in the component hierarchy and a diagnosis is made.
    [00145] During the engineering phase, GUI 805 is used to edit the HFM nodes and the graphics of the BIMs. It first identifies all objects in the IFC HVAC node and retrieves the attributes useful for creating estimation models. It also creates connectivity between nodes other than IFC connectivity data. For parameters, conditions, missing, and switches not found in BIM, the tool provides an interface for the user to enter data. Otherwise, the default settings are used.
    [00146] This information is compiled 807 together and an HFM model in XML format is generated. The XML based on the HFM graph is loaded into an FDD HVAC system, which compiles the graphical model for an FDD 815 engine based on its execution phase compilation capability and an existing object library for the HFM nodes. The FDD 815 engine can be built into the Construction Management System (BMS) to perform defect detection and diagnosis using the rules modeled on each node and the propagation of defects.
    [00147] The FDD 815 engine can be implemented using Specification Description Language (SDL) modeling tools, SDL specifies a hierarchical structure of autonomous communications processes that can be interpreted as agents. Flow nodes can be mapped directly to SDL processes. The advantage of SDL models is that a model can be automatically translated into C code and compiled for executable prototype systems.
    [00148] One or more embodiments of the present invention have been described. However, it should be understood that various modifications can be made without departing from the spirit or scope of the invention.
    权利要求:
    Claims (16)
    [0001]
    1. Method of using a Thermal Flow Model (HFM) graph to detect defects in the HVAC system for a building, characterized by the fact that it understands, translating the descriptions of the formal HVAC system from a model of Information of Construction (BIM) for construction as HFM nodes; retrieve BIM HVAC component attributes for each HFM node; retrieve predefined HFM nodes from an HFM node library that corresponds to the types of BIM HVAC equipment; create connectivity between the HFM nodes retrieved from the BIM connectivity data; compile the HFM nodes for an HFM chart; insert real-time data from the building HVAC control system to the HFM graph for failure detection; detect faults in the building HVAC system using node rules that are conditioned inequalities based on node specific configuration data that define the functionality of a physical HVAC component; and map the rule violations of the HFM nodes to the failures of the construction HVAC control system.
    [0002]
    2. Method, according to claim 1, characterized by the fact that it additionally comprises editing the HFM graph for missing parameters, conditions and switches not found in BIM.
    [0003]
    3. Method, according to claim 1, characterized by the fact that the HFM graph is a one-to-one correspondence with a component structure of the HVAC system of physical construction and the nodes of the HFM graph model the dynamic physical behavior the parameters of temperature T, humidity H, flow rate Q and pressure P of the HVAC components of physical construction as derived from BIM.
    [0004]
    4. Method, according to claim 1, characterized by the fact that each node of the HFM graph evaluates physical downstream and upstream values that correspond to its construction HVAC components with the instrument sensor data dynamic output and control of the construction HVAC control system and propagates the values of physical behavior upstream and downstream through the HFM graph according to the evaluated parameter variations.
    [0005]
    5. Modern Heating, Ventilation and Air Conditioning (HVAC) failure detection system for a building characterized by the fact that it comprises means configured to translate the descriptions of the formal HVAC system of a Building Information Model (BIM) for building like HFM nodes; retrieve BIM HVAC component attributes for each HFM node; retrieve predefined HFM nodes from an HFM node library that corresponds to the types of BIM HVAC equipment; create connectivity between the HFM nodes retrieved from the BIM connectivity data; compile the HFM nodes for an HFM chart; insert real-time data from the building HVAC control system to the HFM graph for failure detection; detect faults in the building HVAC system using node rules that are conditioned inequalities based on node specific configuration data that define the functionality of a physical HVAC component; and map the rule violations of the HFM nodes to the failures of the construction HVAC control system.
    [0006]
    6. System, according to claim 5, characterized by the fact that it also comprises an interface configured to access a Building Information Model (BIM) file library (803) and import the BIM files from the building HVAC system; an HFM node library (803) configured to store a plurality of different predefined HFM node types in which an HFM node models the dynamic physical behavior parameters of the air and water flows in the predefined HVAC components as derived from the BIM files; a Graphical User Interface (GUI) (805) configured to insert and edit HFM nodes and connection configuration data during an HFM graph assembly (909); a compiler (807) coupled to an interface and GUI (805), configured to compose the BIM file data together with the additional connection configuration data; and a Defect Detection and Diagnosis (FDD) generator (811) coupled to the compiler (807) and the HFM node library (813), configured to compare the BIM file types for building HVAC systems with the HFM node types predefined and select HFM nodes that correspond and generate an HFM graph in which the HFM graph is by mass airflow path corresponding to the components and behavior of the construction HVAC system.
    [0007]
    7. The system according to claim 6, further comprising an FDD motor (815) configured to justify the HFM graph as an execution phase system for the construction HVAC control system; and an interface (817) configured to access the construction HVAC control system, where the FDD engine (815) runs the HFM graph with the HVAC control system process and variable control data and applies node rules to detect defects in the HVAC system.
    [0008]
    8. System according to claim 6, characterized by the fact that the parameters of dynamic physical behavior are temperature T, humidity H, flow Q and pressure P.
    [0009]
    9. System, according to claim 7, characterized by the fact that the HFM node estimates the parameters of physical behavior upstream and downstream of the HVAC components connected with the dynamic output data of the HVAC control system using the instrument process and control variables and propagate the results through the HFM graph as parameter variations.
    [0010]
    10. System, according to claim 6, characterized by the fact that BIM is an Industrial Foundation Class (IFC) and the BIM files also comprise the Industrial Foundation Class (IFC) files.
    [0011]
    11. System according to claim 10, characterized by the fact that the IFC files provide a set of definitions for the types of IFC object elements found in the HVAC control and mechanical system, and a text-based structure for storing the object element type definitions in a data file, where IFC object elements define the basic properties and attributes used in the HFM node chart.
    [0012]
    12. System according to claim 11, characterized in that the IFC object element comprises two parts, an IFC element and an IFC port, in which an element is a component that can be coupled to an adjacent element using a or more ports, and IFC elements include flow segments, flow fittings, mobile devices, and flow controllers.
    [0013]
    13. System according to claim 12, characterized by the fact that the compiler (807) is also configured to identify all HFM nodes of the IFC elements and retrieve attributes to create node estimation models and create connectivity between different nodes of IFC connectivity data.
    [0014]
    14. System, according to claim 6, characterized by the fact that the HFM graphic is generated in the Extensible Markup Language (XML) format.
    [0015]
    15. System according to claim 6, characterized by the fact that the HFM node library (813) contains HFM node object models that can be hierarchically composed and decomposed.
    [0016]
    16. System, according to claim 7, characterized by the fact that for each HFM node, based on the received measurements and flow information from the upstream and downstream HFM nodes, the FDD motor (815) is also configured to execute state estimation and propagate the resulting parameter variations to adjacent nodes
    类似技术:
    公开号 | 公开日 | 专利标题
    BR112012009211B1|2020-11-03|method of using a thermal flow model chart to detect defects in the hvac system for a construction and fault detection system
    Yu et al.2014|A review of fault detection and diagnosis methodologies on air-handling units
    US8478433B2|2013-07-02|Fault detection systems and methods for self-optimizing heating, ventilation, and air conditioning controls
    Magoulès et al.2013|Development of an RDP neural network for building energy consumption fault detection and diagnosis
    US7024258B2|2006-04-04|System and method for model-based control of a building fluid distribution system
    US20150178421A1|2015-06-25|Systems for and methods of modeling, step-testing, and adaptively controlling in-situ building components
    Bengea et al.2015|Fault-tolerant optimal control of a building HVAC system
    Wall et al.2011|A Dynamic Machine Learning-based Technique for Automated Fault Detection in HVAC Systems.
    Ranade et al.2020|A computationally efficient method for fault diagnosis of fan-coil unit terminals in building Heating Ventilation and Air Conditioning systems
    Andriamamonjy et al.2018|An auto-deployed model-based fault detection and diagnosis approach for Air Handling Units using BIM and Modelica
    Zimmermann et al.2012|Automatic HVAC fault detection and diagnosis system generation based on heat flow models
    Underwood2000|Robust control of HVAC plant I: modelling
    Papadopoulos et al.2020|Scalable distributed sensor fault diagnosis for smart buildings
    Leong2019|Fault detection and diagnosis of air handling unit: A review
    Di Benedetto et al.2008|Automatic verification of wireless control in a mining ventilation system
    KR20200092478A|2020-08-04|Method for controlling economizer air conditioning system
    Provan2011|Generating reduced-order diagnosis models for hvac systems
    Mattera et al.2018|Fault detection and diagnostics in ventilation units using linear regression virtual sensors
    Khire et al.2013|Model based failure mode effect analysis on whole building energy performance
    Ploennigs et al.2017|From semantic models to cognitive buildings
    Zimmermann et al.2010|Heat flow modeling of HVAC systems for fault detection and diagnosis
    Xu et al.2006|A library of HVAC component models for use in automated diagnostics
    Shi et al.2018|Using building performance simulation for fault impact evaluation
    Taal et al.2020|P&ID-based automated fault identification for energy performance diagnosis in HVAC systems: 4S3F method, development of DBN models and application to an ATES system
    Andriamamonjya et al.2018|4.3 An auto-deployed model-based fault detection and diagnosis for AHU using BIM and Modelica
    同族专利:
    公开号 | 公开日
    US20110093424A1|2011-04-21|
    CN102687085A|2012-09-19|
    KR101401600B1|2014-06-03|
    CA2777985C|2015-03-31|
    US8606554B2|2013-12-10|
    KR20120069737A|2012-06-28|
    CN102687085B|2015-09-30|
    EP2491464A1|2012-08-29|
    MX2012004529A|2012-06-12|
    EP2491464B1|2016-09-14|
    BR112012009211A2|2016-08-16|
    CA2777985A1|2011-04-28|
    WO2011049890A1|2011-04-28|
    引用文献:
    公开号 | 申请日 | 公开日 | 申请人 | 专利标题
    US5209398A|1992-09-02|1993-05-11|Johnson Service Company|Model-based thermobalance with feedback|
    US6962435B2|2000-12-15|2005-11-08|Abb Technology Ltd.|Condition diagnosing|
    US7383158B2|2002-04-16|2008-06-03|Trane International Inc.|HVAC service tool with internet capability|
    CN100501720C|2004-01-20|2009-06-17|开利公司|Failure mode for heating, aeration and air conditioning system|
    CN100437510C|2004-01-20|2008-11-26|开利公司|Ordered record of system-wide fault in an hvac system|
    US6981383B2|2004-01-20|2006-01-03|Carrier Corporation|Zone damper fault detection in an HVAC system|
    US20060009862A1|2004-06-28|2006-01-12|Raphael Imhof|Method and apparatus for accessing a building system model|
    US7494536B2|2005-01-04|2009-02-24|Carrier Corporation|Method for detecting a fault in an HVAC system|
    US7444251B2|2006-08-01|2008-10-28|Mitsubishi Electric Research Laboratories, Inc.|Detecting and diagnosing faults in HVAC equipment|
    EP2057517B1|2006-09-28|2013-06-12|Fisher-Rosemount Systems, Inc.|Abnormal situation prevention in a heat exchanger|
    US7853392B2|2007-01-26|2010-12-14|General Electric Company|Systems and methods for initializing dynamic model states using a Kalman filter|
    US9020647B2|2009-03-27|2015-04-28|Siemens Industry, Inc.|System and method for climate control set-point optimization based on individual comfort|
    EP2302470A3|2009-09-29|2014-06-11|Honeywell International Inc.|Systems and methods for configuring a building management system|
    US20120245968A1|2011-03-21|2012-09-27|Honeywell International Inc.|Building system control and equipment fault and degradation monetization and prioritization|US9456054B2|2008-05-16|2016-09-27|Palo Alto Research Center Incorporated|Controlling the spread of interests and content in a content centric network|
    US8923293B2|2009-10-21|2014-12-30|Palo Alto Research Center Incorporated|Adaptive multi-interface use for content networking|
    US9977409B2|2011-03-02|2018-05-22|Carrier Corporation|SPC fault detection and diagnostics algorithm|
    JP5093378B2|2011-05-12|2012-12-12|ダイキン工業株式会社|Ventilation system|
    US10331510B2|2011-05-23|2019-06-25|Siemens Corporation|Simulation based fault diagnosis using extended heat flow models|
    US9366448B2|2011-06-20|2016-06-14|Honeywell International Inc.|Method and apparatus for configuring a filter change notification of an HVAC controller|
    US8892223B2|2011-09-07|2014-11-18|Honeywell International Inc.|HVAC controller including user interaction log|
    KR101285023B1|2011-11-30|2013-07-10|엘지전자 주식회사|An installation guide system for an air conditioner and a using method the same|
    US9206993B2|2011-12-14|2015-12-08|Honeywell International Inc.|HVAC controller with utility saver switch diagnostic feature|
    US10747243B2|2011-12-14|2020-08-18|Ademco Inc.|HVAC controller with HVAC system failure detection|
    US8902071B2|2011-12-14|2014-12-02|Honeywell International Inc.|HVAC controller with HVAC system fault detection|
    US9002523B2|2011-12-14|2015-04-07|Honeywell International Inc.|HVAC controller with diagnostic alerts|
    US10533761B2|2011-12-14|2020-01-14|Ademco Inc.|HVAC controller with fault sensitivity|
    US20130158720A1|2011-12-15|2013-06-20|Honeywell International Inc.|Hvac controller with performance log|
    JP5884125B2|2012-02-07|2016-03-15|ナガノサイエンス株式会社|Anomaly detection apparatus and environmental test apparatus equipped with the same|
    JP5576455B2|2012-02-20|2014-08-20|ソリデオシステムズカンパニーリミテッド|Method for providing data contained in BIM data file, recording medium recording the same, and system including the same|
    US9442500B2|2012-03-08|2016-09-13|Honeywell International Inc.|Systems and methods for associating wireless devices of an HVAC system|
    US10452084B2|2012-03-14|2019-10-22|Ademco Inc.|Operation of building control via remote device|
    US9488994B2|2012-03-29|2016-11-08|Honeywell International Inc.|Method and system for configuring wireless sensors in an HVAC system|
    US10146891B2|2012-03-30|2018-12-04|Honeywell International Inc.|Extracting data from a 3D geometric model by geometry analysis|
    US9513643B2|2012-04-23|2016-12-06|Emerson Climate Technologies Retail Solutions, Inc.|Building device cluster data display with thumbnail graphical display interface|
    US8954479B2|2012-06-13|2015-02-10|International Business Machines Corporation|End-to-end interoperability and workflows from building architecture design to one or more simulations|
    DE102012210041A1|2012-06-14|2013-12-19|Robert Bosch Gmbh|Pressure sensor and system for controlling a ventilation device|
    US9594384B2|2012-07-26|2017-03-14|Honeywell International Inc.|Method of associating an HVAC controller with an external web service|
    US9477239B2|2012-07-26|2016-10-25|Honeywell International Inc.|HVAC controller with wireless network based occupancy detection and control|
    US9280546B2|2012-10-31|2016-03-08|Palo Alto Research Center Incorporated|System and method for accessing digital content using a location-independent name|
    US9396293B2|2012-11-06|2016-07-19|Cenergistic Llc|Adjustment simulation method for energy consumption|
    EP2920718B1|2012-11-16|2018-01-10|Honeywell International Inc.|Fuse multiple drawings into an equipmentmodel|
    US9400800B2|2012-11-19|2016-07-26|Palo Alto Research Center Incorporated|Data transport by named content synchronization|
    CN103246951A|2012-12-11|2013-08-14|中建三局第一建设工程有限责任公司|Intelligent electromechanical equipment management system and method based on BIM |
    US10430839B2|2012-12-12|2019-10-01|Cisco Technology, Inc.|Distributed advertisement insertion in content-centric networks|
    CN104871097B|2012-12-13|2018-05-18|Abb 技术有限公司|For monitoring and/or diagnosing the system and method for industrial plant production line operation|
    EP2938956B1|2012-12-28|2020-10-07|Schneider Electric IT Corporation|Method for air flow fault and cause identification|
    US10094585B2|2013-01-25|2018-10-09|Honeywell International Inc.|Auto test for delta T diagnostics in an HVAC system|
    KR20140096717A|2013-01-29|2014-08-06|한국전자통신연구원|BIM-based building energy management apparatus and method|
    US9244471B2|2013-03-14|2016-01-26|Siemens Industry, Inc.|Methods and systems for remotely monitoring and controlling HVAC units|
    US9978025B2|2013-03-20|2018-05-22|Cisco Technology, Inc.|Ordered-element naming for name-based packet forwarding|
    US20140324387A1|2013-04-29|2014-10-30|Enernoc, Inc.|Building management system data normalization and standardization mechanism|
    US9935791B2|2013-05-20|2018-04-03|Cisco Technology, Inc.|Method and system for name resolution across heterogeneous architectures|
    US9185120B2|2013-05-23|2015-11-10|Palo Alto Research Center Incorporated|Method and system for mitigating interest flooding attacks in content-centric networks|
    JP6179196B2|2013-05-31|2017-08-16|富士通株式会社|Data center|
    US9714771B1|2013-07-30|2017-07-25|Alarm.Com Incorporated|Dynamically programmable thermostat|
    US9444722B2|2013-08-01|2016-09-13|Palo Alto Research Center Incorporated|Method and apparatus for configuring routing paths in a custodian-based routing architecture|
    CN104566762B|2013-10-12|2018-03-20|珠海格力电器股份有限公司|The air outlet abnormality monitoring method and system of dehumidifier|
    KR101525680B1|2013-10-25|2015-06-03|단국대학교 산학협력단|Integrated building control system for design and operation stage|
    US9407549B2|2013-10-29|2016-08-02|Palo Alto Research Center Incorporated|System and method for hash-based forwarding of packets with hierarchically structured variable-length identifiers|
    US9276840B2|2013-10-30|2016-03-01|Palo Alto Research Center Incorporated|Interest messages with a payload for a named data network|
    US9282050B2|2013-10-30|2016-03-08|Palo Alto Research Center Incorporated|System and method for minimum path MTU discovery in content centric networks|
    US9401864B2|2013-10-31|2016-07-26|Palo Alto Research Center Incorporated|Express header for packets with hierarchically structured variable-length identifiers|
    US9311377B2|2013-11-13|2016-04-12|Palo Alto Research Center Incorporated|Method and apparatus for performing server handoff in a name-based content distribution system|
    US10129365B2|2013-11-13|2018-11-13|Cisco Technology, Inc.|Method and apparatus for pre-fetching remote content based on static and dynamic recommendations|
    US10101801B2|2013-11-13|2018-10-16|Cisco Technology, Inc.|Method and apparatus for prefetching content in a data stream|
    US10089655B2|2013-11-27|2018-10-02|Cisco Technology, Inc.|Method and apparatus for scalable data broadcasting|
    US9503358B2|2013-12-05|2016-11-22|Palo Alto Research Center Incorporated|Distance-based routing in an information-centric network|
    US10649418B2|2013-12-11|2020-05-12|Ademco Inc.|Building automation controller with configurable audio/visual cues|
    US9558106B1|2013-12-19|2017-01-31|Amazon Technologies, Inc.|Testing service with control testing|
    CN104765354B|2014-01-10|2018-02-09|北京博锐尚格节能技术股份有限公司|A kind of method for diagnosing faults, the apparatus and system of sensor and executive component|
    US9379979B2|2014-01-14|2016-06-28|Palo Alto Research Center Incorporated|Method and apparatus for establishing a virtual interface for a set of mutual-listener devices|
    US10098051B2|2014-01-22|2018-10-09|Cisco Technology, Inc.|Gateways and routing in software-defined manets|
    US10172068B2|2014-01-22|2019-01-01|Cisco Technology, Inc.|Service-oriented routing in software-defined MANETs|
    US9531679B2|2014-02-06|2016-12-27|Palo Alto Research Center Incorporated|Content-based transport security for distributed producers|
    US9954678B2|2014-02-06|2018-04-24|Cisco Technology, Inc.|Content-based transport security|
    US9678998B2|2014-02-28|2017-06-13|Cisco Technology, Inc.|Content name resolution for information centric networking|
    US9817922B2|2014-03-01|2017-11-14|Anguleris Technologies, Llc|Method and system for creating 3D models from 2D data for building information modeling |
    US9782936B2|2014-03-01|2017-10-10|Anguleris Technologies, Llc|Method and system for creating composite 3D models for building information modeling |
    US10089651B2|2014-03-03|2018-10-02|Cisco Technology, Inc.|Method and apparatus for streaming advertisements in a scalable data broadcasting system|
    US9836540B2|2014-03-04|2017-12-05|Cisco Technology, Inc.|System and method for direct storage access in a content-centric network|
    US9473405B2|2014-03-10|2016-10-18|Palo Alto Research Center Incorporated|Concurrent hashes and sub-hashes on data streams|
    US9391896B2|2014-03-10|2016-07-12|Palo Alto Research Center Incorporated|System and method for packet forwarding using a conjunctive normal form strategy in a content-centric network|
    US9626413B2|2014-03-10|2017-04-18|Cisco Systems, Inc.|System and method for ranking content popularity in a content-centric network|
    CN104764173B|2014-03-11|2018-11-13|北京博锐尚格节能技术股份有限公司|A kind of monitoring method of heating ventilation air-conditioning system, apparatus and system|
    US9407432B2|2014-03-19|2016-08-02|Palo Alto Research Center Incorporated|System and method for efficient and secure distribution of digital content|
    US9916601B2|2014-03-21|2018-03-13|Cisco Technology, Inc.|Marketplace for presenting advertisements in a scalable data broadcasting system|
    US9363179B2|2014-03-26|2016-06-07|Palo Alto Research Center Incorporated|Multi-publisher routing protocol for named data networks|
    US9363086B2|2014-03-31|2016-06-07|Palo Alto Research Center Incorporated|Aggregate signing of data in content centric networking|
    US9716622B2|2014-04-01|2017-07-25|Cisco Technology, Inc.|System and method for dynamic name configuration in content-centric networks|
    US9473576B2|2014-04-07|2016-10-18|Palo Alto Research Center Incorporated|Service discovery using collection synchronization with exact names|
    US9390289B2|2014-04-07|2016-07-12|Palo Alto Research Center Incorporated|Secure collection synchronization using matched network names|
    US10075521B2|2014-04-07|2018-09-11|Cisco Technology, Inc.|Collection synchronization using equality matched network names|
    US9451032B2|2014-04-10|2016-09-20|Palo Alto Research Center Incorporated|System and method for simple service discovery in content-centric networks|
    US9203885B2|2014-04-28|2015-12-01|Palo Alto Research Center Incorporated|Method and apparatus for exchanging bidirectional streams over a content centric network|
    US9992281B2|2014-05-01|2018-06-05|Cisco Technology, Inc.|Accountable content stores for information centric networks|
    US9609014B2|2014-05-22|2017-03-28|Cisco Systems, Inc.|Method and apparatus for preventing insertion of malicious content at a named data network router|
    US9455835B2|2014-05-23|2016-09-27|Palo Alto Research Center Incorporated|System and method for circular link resolution with hash-based names in content-centric networks|
    US9276751B2|2014-05-28|2016-03-01|Palo Alto Research Center Incorporated|System and method for circular link resolution with computable hash-based names in content-centric networks|
    US10156378B2|2014-05-29|2018-12-18|Emerson Climate Technologies, Inc.|HVAC system remote monitoring and diagnosis of refrigerant line obstruction|
    US9537719B2|2014-06-19|2017-01-03|Palo Alto Research Center Incorporated|Method and apparatus for deploying a minimal-cost CCN topology|
    US9467377B2|2014-06-19|2016-10-11|Palo Alto Research Center Incorporated|Associating consumer states with interests in a content-centric network|
    US9516144B2|2014-06-19|2016-12-06|Palo Alto Research Center Incorporated|Cut-through forwarding of CCNx message fragments with IP encapsulation|
    KR20160001023A|2014-06-26|2016-01-06|삼성전자주식회사|Method and apparatus for detecting building information|
    US9426113B2|2014-06-30|2016-08-23|Palo Alto Research Center Incorporated|System and method for managing devices over a content centric network|
    US9699198B2|2014-07-07|2017-07-04|Cisco Technology, Inc.|System and method for parallel secure content bootstrapping in content-centric networks|
    US9621354B2|2014-07-17|2017-04-11|Cisco Systems, Inc.|Reconstructable content objects|
    US9959156B2|2014-07-17|2018-05-01|Cisco Technology, Inc.|Interest return control message|
    US9590887B2|2014-07-18|2017-03-07|Cisco Systems, Inc.|Method and system for keeping interest alive in a content centric network|
    US9729616B2|2014-07-18|2017-08-08|Cisco Technology, Inc.|Reputation-based strategy for forwarding and responding to interests over a content centric network|
    US9535968B2|2014-07-21|2017-01-03|Palo Alto Research Center Incorporated|System for distributing nameless objects using self-certifying names|
    US9882964B2|2014-08-08|2018-01-30|Cisco Technology, Inc.|Explicit strategy feedback in name-based forwarding|
    US9729662B2|2014-08-11|2017-08-08|Cisco Technology, Inc.|Probabilistic lazy-forwarding technique without validation in a content centric network|
    US9503365B2|2014-08-11|2016-11-22|Palo Alto Research Center Incorporated|Reputation-based instruction processing over an information centric network|
    US9391777B2|2014-08-15|2016-07-12|Palo Alto Research Center Incorporated|System and method for performing key resolution over a content centric network|
    US9800637B2|2014-08-19|2017-10-24|Cisco Technology, Inc.|System and method for all-in-one content stream in content-centric networks|
    US9467492B2|2014-08-19|2016-10-11|Palo Alto Research Center Incorporated|System and method for reconstructable all-in-one content stream|
    US9497282B2|2014-08-27|2016-11-15|Palo Alto Research Center Incorporated|Network coding for content-centric network|
    US10204013B2|2014-09-03|2019-02-12|Cisco Technology, Inc.|System and method for maintaining a distributed and fault-tolerant state over an information centric network|
    US9553812B2|2014-09-09|2017-01-24|Palo Alto Research Center Incorporated|Interest keep alives at intermediate routers in a CCN|
    CN107111748B|2014-10-01|2020-09-22|Abb瑞士股份有限公司|Method and system for configuring devices of a control system based on engineering graphical objects|
    US10274915B2|2014-10-22|2019-04-30|Carrier Corporation|Scalable cyber-physical structure management|
    US10069933B2|2014-10-23|2018-09-04|Cisco Technology, Inc.|System and method for creating virtual interfaces based on network characteristics|
    CN107111286B|2014-11-12|2021-11-19|开利公司|Automated functional testing for diagnostics and control|
    US9590948B2|2014-12-15|2017-03-07|Cisco Systems, Inc.|CCN routing using hardware-assisted hash tables|
    US9536059B2|2014-12-15|2017-01-03|Palo Alto Research Center Incorporated|Method and system for verifying renamed content using manifests in a content centric network|
    US10237189B2|2014-12-16|2019-03-19|Cisco Technology, Inc.|System and method for distance-based interest forwarding|
    US9846881B2|2014-12-19|2017-12-19|Palo Alto Research Center Incorporated|Frugal user engagement help systems|
    US10003520B2|2014-12-22|2018-06-19|Cisco Technology, Inc.|System and method for efficient name-based content routing using link-state information in information-centric networks|
    US9473475B2|2014-12-22|2016-10-18|Palo Alto Research Center Incorporated|Low-cost authenticated signing delegation in content centric networking|
    US9660825B2|2014-12-24|2017-05-23|Cisco Technology, Inc.|System and method for multi-source multicasting in content-centric networks|
    US9946743B2|2015-01-12|2018-04-17|Cisco Technology, Inc.|Order encoded manifests in a content centric network|
    US9602596B2|2015-01-12|2017-03-21|Cisco Systems, Inc.|Peer-to-peer sharing in a content centric network|
    US9916457B2|2015-01-12|2018-03-13|Cisco Technology, Inc.|Decoupled name security binding for CCN objects|
    US9832291B2|2015-01-12|2017-11-28|Cisco Technology, Inc.|Auto-configurable transport stack|
    US9954795B2|2015-01-12|2018-04-24|Cisco Technology, Inc.|Resource allocation using CCN manifests|
    US9462006B2|2015-01-21|2016-10-04|Palo Alto Research Center Incorporated|Network-layer application-specific trust model|
    US9552493B2|2015-02-03|2017-01-24|Palo Alto Research Center Incorporated|Access control framework for information centric networking|
    US10333840B2|2015-02-06|2019-06-25|Cisco Technology, Inc.|System and method for on-demand content exchange with adaptive naming in information-centric networks|
    CN104614126A|2015-02-13|2015-05-13|郑州能通网络技术有限公司|Leakage monitoring method for building in steel-frame structure based on BIMtechnology|
    US10075401B2|2015-03-18|2018-09-11|Cisco Technology, Inc.|Pending interest table behavior|
    US10116605B2|2015-06-22|2018-10-30|Cisco Technology, Inc.|Transport stack name scheme and identity management|
    US10075402B2|2015-06-24|2018-09-11|Cisco Technology, Inc.|Flexible command and control in content centric networks|
    US10701038B2|2015-07-27|2020-06-30|Cisco Technology, Inc.|Content negotiation in a content centric network|
    US9986034B2|2015-08-03|2018-05-29|Cisco Technology, Inc.|Transferring state in content centric network stacks|
    US10610144B2|2015-08-19|2020-04-07|Palo Alto Research Center Incorporated|Interactive remote patient monitoring and condition management intervention system|
    US9832123B2|2015-09-11|2017-11-28|Cisco Technology, Inc.|Network named fragments in a content centric network|
    US10355999B2|2015-09-23|2019-07-16|Cisco Technology, Inc.|Flow control with network named fragments|
    US9977809B2|2015-09-24|2018-05-22|Cisco Technology, Inc.|Information and data framework in a content centric network|
    US10313227B2|2015-09-24|2019-06-04|Cisco Technology, Inc.|System and method for eliminating undetected interest looping in information-centric networks|
    US10454820B2|2015-09-29|2019-10-22|Cisco Technology, Inc.|System and method for stateless information-centric networking|
    US10263965B2|2015-10-16|2019-04-16|Cisco Technology, Inc.|Encrypted CCNx|
    US9794238B2|2015-10-29|2017-10-17|Cisco Technology, Inc.|System for key exchange in a content centric network|
    US10009446B2|2015-11-02|2018-06-26|Cisco Technology, Inc.|Header compression for CCN messages using dictionary learning|
    US9807205B2|2015-11-02|2017-10-31|Cisco Technology, Inc.|Header compression for CCN messages using dictionary|
    US10021222B2|2015-11-04|2018-07-10|Cisco Technology, Inc.|Bit-aligned header compression for CCN messages using dictionary|
    US10867282B2|2015-11-06|2020-12-15|Anguleris Technologies, Llc|Method and system for GPS enabled model and site interaction and collaboration for BIM and other design platforms|
    US10949805B2|2015-11-06|2021-03-16|Anguleris Technologies, Llc|Method and system for native object collaboration, revision and analytics for BIM and other design platforms|
    JP6249009B2|2015-11-12|2017-12-20|株式会社デンソー|Abnormality diagnosis device|
    US10097521B2|2015-11-20|2018-10-09|Cisco Technology, Inc.|Transparent encryption in a content centric network|
    US9912776B2|2015-12-02|2018-03-06|Cisco Technology, Inc.|Explicit content deletion commands in a content centric network|
    US10097346B2|2015-12-09|2018-10-09|Cisco Technology, Inc.|Key catalogs in a content centric network|
    US10078062B2|2015-12-15|2018-09-18|Palo Alto Research Center Incorporated|Device health estimation by combining contextual information with sensor data|
    EP3390972B1|2015-12-18|2020-10-07|Vertiv Corporation|System and method for inferring or prompting hvac actions based on large data standard deviation based metric|
    US10257271B2|2016-01-11|2019-04-09|Cisco Technology, Inc.|Chandra-Toueg consensus in a content centric network|
    US10429808B2|2016-01-19|2019-10-01|Honeywell International Inc.|System that automatically infers equipment details from controller configuration details|
    US9949301B2|2016-01-20|2018-04-17|Palo Alto Research Center Incorporated|Methods for fast, secure and privacy-friendly internet connection discovery in wireless networks|
    US10305864B2|2016-01-25|2019-05-28|Cisco Technology, Inc.|Method and system for interest encryption in a content centric network|
    US10043016B2|2016-02-29|2018-08-07|Cisco Technology, Inc.|Method and system for name encryption agreement in a content centric network|
    US10742596B2|2016-03-04|2020-08-11|Cisco Technology, Inc.|Method and system for reducing a collision probability of hash-based names using a publisher identifier|
    US10003507B2|2016-03-04|2018-06-19|Cisco Technology, Inc.|Transport session state protocol|
    US10038633B2|2016-03-04|2018-07-31|Cisco Technology, Inc.|Protocol to query for historical network information in a content centric network|
    US10051071B2|2016-03-04|2018-08-14|Cisco Technology, Inc.|Method and system for collecting historical network information in a content centric network|
    US9832116B2|2016-03-14|2017-11-28|Cisco Technology, Inc.|Adjusting entries in a forwarding information base in a content centric network|
    US10212196B2|2016-03-16|2019-02-19|Cisco Technology, Inc.|Interface discovery and authentication in a name-based network|
    US10067948B2|2016-03-18|2018-09-04|Cisco Technology, Inc.|Data deduping in content centric networking manifests|
    US10091330B2|2016-03-23|2018-10-02|Cisco Technology, Inc.|Interest scheduling by an information and data framework in a content centric network|
    US10033639B2|2016-03-25|2018-07-24|Cisco Technology, Inc.|System and method for routing packets in a content centric network using anonymous datagrams|
    US10320760B2|2016-04-01|2019-06-11|Cisco Technology, Inc.|Method and system for mutating and caching content in a content centric network|
    US9930146B2|2016-04-04|2018-03-27|Cisco Technology, Inc.|System and method for compressing content centric networking messages|
    US10425503B2|2016-04-07|2019-09-24|Cisco Technology, Inc.|Shared pending interest table in a content centric network|
    US10027578B2|2016-04-11|2018-07-17|Cisco Technology, Inc.|Method and system for routable prefix queries in a content centric network|
    US10216160B2|2016-04-21|2019-02-26|Honeywell International Inc.|Matching a building automation algorithm to a building automation system|
    US10404450B2|2016-05-02|2019-09-03|Cisco Technology, Inc.|Schematized access control in a content centric network|
    US10320675B2|2016-05-04|2019-06-11|Cisco Technology, Inc.|System and method for routing packets in a stateless content centric network|
    US10547589B2|2016-05-09|2020-01-28|Cisco Technology, Inc.|System for implementing a small computer systems interface protocol over a content centric network|
    US10084764B2|2016-05-13|2018-09-25|Cisco Technology, Inc.|System for a secure encryption proxy in a content centric network|
    US10063414B2|2016-05-13|2018-08-28|Cisco Technology, Inc.|Updating a transport stack in a content centric network|
    EP3465021A1|2016-06-03|2019-04-10|Belimo Holding AG|Method and computer system for monitoring an hvac system|
    US10103989B2|2016-06-13|2018-10-16|Cisco Technology, Inc.|Content object return messages in a content centric network|
    US10305865B2|2016-06-21|2019-05-28|Cisco Technology, Inc.|Permutation-based content encryption with manifests in a content centric network|
    US10148572B2|2016-06-27|2018-12-04|Cisco Technology, Inc.|Method and system for interest groups in a content centric network|
    US10009266B2|2016-07-05|2018-06-26|Cisco Technology, Inc.|Method and system for reference counted pending interest tables in a content centric network|
    US9992097B2|2016-07-11|2018-06-05|Cisco Technology, Inc.|System and method for piggybacking routing information in interests in a content centric network|
    US10488062B2|2016-07-22|2019-11-26|Ademco Inc.|Geofence plus schedule for a building controller|
    US10122624B2|2016-07-25|2018-11-06|Cisco Technology, Inc.|System and method for ephemeral entries in a forwarding information base in a content centric network|
    US10069729B2|2016-08-08|2018-09-04|Cisco Technology, Inc.|System and method for throttling traffic based on a forwarding information base in a content centric network|
    US10956412B2|2016-08-09|2021-03-23|Cisco Technology, Inc.|Method and system for conjunctive normal form attribute matching in a content centric network|
    US10033642B2|2016-09-19|2018-07-24|Cisco Technology, Inc.|System and method for making optimal routing decisions based on device-specific parameters in a content centric network|
    US10212248B2|2016-10-03|2019-02-19|Cisco Technology, Inc.|Cache management on high availability routers in a content centric network|
    US10447805B2|2016-10-10|2019-10-15|Cisco Technology, Inc.|Distributed consensus in a content centric network|
    US10135948B2|2016-10-31|2018-11-20|Cisco Technology, Inc.|System and method for process migration in a content centric network|
    US10243851B2|2016-11-21|2019-03-26|Cisco Technology, Inc.|System and method for forwarder connection information in a content centric network|
    KR101876114B1|2017-01-11|2018-07-06|서울시립대학교 산학협력단|Terminal, server, system for 3d modeling and 3d modeling method using the same|
    US10458671B2|2017-04-11|2019-10-29|Ademco Inc.|Building control device having probability distribution based sensing|
    US10755002B2|2017-05-12|2020-08-25|Ge-Hitachi Nuclear Energy Americas Llc|Method and apparatus for facilitating creation of simulation model|
    US10536294B2|2017-07-17|2020-01-14|Midea America Corp.|Computer-based platform for quality management of home devices|
    US10677485B2|2017-09-19|2020-06-09|Honeywell International Inc.|Determining the cause of a fault in an HVAC system|
    WO2020075262A1|2018-10-11|2020-04-16|三菱電機株式会社|Failure sign detection device|
    US11030709B2|2018-10-29|2021-06-08|DIGIBILT, Inc.|Method and system for automatically creating and assigning assembly labor activitiesto a bill of materials |
    US10997553B2|2018-10-29|2021-05-04|DIGIBILT, Inc.|Method and system for automatically creating a bill of materials|
    WO2021142694A1|2020-01-16|2021-07-22|Honeywell International Inc.|Root cause analytics of hvac faults|
    CN111709453A|2020-05-22|2020-09-25|成都飞机工业（集团）有限责任公司|Online fault diagnosis method for electrical system of aircraft engine|
    法律状态:
    2019-01-08| B06F| Objections, documents and/or translations needed after an examination request according [chapter 6.6 patent gazette]|
    2019-07-30| B06U| Preliminary requirement: requests with searches performed by other patent offices: procedure suspended [chapter 6.21 patent gazette]|
    2020-06-16| B09A| Decision: intention to grant [chapter 9.1 patent gazette]|
    2020-11-03| B16A| Patent or certificate of addition of invention granted [chapter 16.1 patent gazette]|Free format text: PRAZO DE VALIDADE: 10 (DEZ) ANOS CONTADOS A PARTIR DE 03/11/2020, OBSERVADAS AS CONDICOES LEGAIS. |
    优先权:
    申请号 | 申请日 | 专利标题
    US25286209P| true| 2009-10-19|2009-10-19|
    US61/252,862|2009-10-19|
    US12/906,186|2010-10-18|
    US12/906,186|US8606554B2|2009-10-19|2010-10-18|Heat flow model for building fault detection and diagnosis|
    PCT/US2010/053101|WO2011049890A1|2009-10-19|2010-10-19|Fault detection in hvac- systems using building information models and heat flow models|
    [返回顶部]