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    SYSTEM AND METHOD OF DETECTION OF REFRIGERANT LEAKAGE.The invention relates to a refrigerant leak detection system. The system can detect a slow leak based on the refrigerant level and system data. The system uses data models, stored or dynamically created, in order to calculate an expected refrigerant level. From the expected refrigerant level and the current refrigerant level and the process control statistical data, a leak can be identified.
    公开号:BR112012011103A2
    申请号:R112012011103-5
    申请日:2010-11-11
    公开日:2020-09-15
    发明作者:E. Todd Clark
    申请人:Emerson Retail Services, Inc.；
    IPC主号:
    专利说明:

    «Invention Patent Descriptive Report for" REFRIGERANT LEAKAGE DETECTION SYSTEM AND METHOD ". ] CROSS REFERENCE TO RELATED APPLICATIONS This application claims the priority of the US Patent Application. No. 12/943 626, filed on November 10, 2010, and claims the benefit of US Provisional Patent Application No. 61/260 222, filed on November 11, 2009. The full description of the above-mentioned applications is incorporated into this reference document.
    FIELD The present invention relates to refrigeration systems and,. more specifically, monitoring the levels of refrigerant in a refrigeration system. »BACKGROUND This section provides background information regarding the present invention that does not necessarily belong to the prior art.
    Refrigeration systems can be essential for many companies. For example, food retailers can rely on refrigerators to ensure the quality and safety of food products. Many other companies may have products or materials that need to be refrigerated or kept at a low temperature. HVAC air conditioning systems allow people to remain comfortable in the places where they shop, work or live. Any failure in these or other refrigeration systems or variation in the performance of the refrigeration systems can affect health, safety and profitability.
    Thus, it can be important to control and maintain the cooling system equipment in order to guarantee its operation at the expected levels.
    It may be in the best interests of refrigeration system users to closely monitor the performance of cooling systems in order to maximize efficiency and reduce operating costs. Generally speaking, users may not have the necessary knowledge to accurately analyze system performance data and relate this data to quality, safety and profitability, as well as knowledge.
    . necessary to monitor the cooling system for performance, maintenance and efficiency.
    SUMMARY This section provides a general summary of the present invention, and is not intended to be a comprehensive presentation of its entire scope or all of its aspects.
    A system for detecting refrigerant leakage from a refrigeration system is described in this document. The system includes a coolant level sensor that detects a coolant level in the refrigeration appliance and generates coolant level data based on the coolant level, and a plurality of system sensors that detect the conditions corresponding to the refrigeration appliance and generate. system data based on detected conditions. The system also includes a model database that stores a plurality of models that define the expected refrigerant levels from the previously registered system data, with each model having an upper control limit and a lower control limit associated with them, and a model selection module that selects a model from the model database based on previously registered system data and system data. The system also comprises a coolant level prediction module that generates an expected coolant level based on system data and the selected model, and a notification module that generates a notification when a difference between the coolant level expected refrigerant and the refrigerant level reading is greater than an upper control limit and less than a lower control limit for at least one consecutive reading.
    In a system characteristic, the system can also comprise a model creation module that creates a model based on the refrigerant level data and the system data.
    In another characteristic of the system, the model created by the model creation module is also dependent on the hours of the day in which the refrigerant level data and the system data were sampled.
    * trados. In another characteristic of the system, the model creation module performs a linear regression in order to determine a non-compensated linear combination of the system data that estimates the coolant level.
    In another feature of the system, the results of linear regression are used in order to determine an error table having entries corresponding to a difference between the estimated refrigerant level and the refrigerant level data at a particular time of day .
    BR In another feature of the system, the model creation module generates a table that stores an hour effect indicating the amount of effect that an hour of the day has on the refrigerant level data for each hour of the day.
    In another feature of the system, the model creation module performs an hourly compensated linear regression effect in order to determine a linear combination of the system data and the hour effect in order to estimate the refrigerant level.
    In another feature of the system, the notification module generates a notification if the refrigerant has been added from the system when the difference between the expected refrigerant level and the refrigerant level reading is greater than the upper control limit in a predetermined number of consecutive readings.
    In another feature of the system, the notification module generates a notification if the refrigerant is leaking from the system when the difference between the expected refrigerant level and the refrigerant level reading is less than the lower control limit in a predetermined number of consecutive readings.
    In another feature of the system, the system data includes an ambient temperature reading, a condenser temperature reading and a discharge pressure reading.
    In another aspect of the present invention, a method for
    - refrigerant leak detection in a refrigeration system is presented. The method comprises the detection of a refrigerant level in the refrigeration system and the generation of refrigerant level data based on the refrigerant level. The method also includes the detection of conditions corresponding to the refrigeration system and the generation of system data based on the conditions detected. The method also includes storing, in a model database, a plurality of models that define the expected refrigerant levels based on previously registered system data. Each of the models has an upper control limit and a lower control limit associated with them.
    'The method further comprises the selection of a model from the model database based on the previously registered system data and system data, and the generation of an expected refrigerant level based on the system data and the selected model. The method also comprises the generation of a notification when a difference between the expected refrigerant level and the refrigerant level reading is greater than an upper control limit and less than a lower control limit by at least consecutive reading.
    In another characteristic of the method, the method also comprises the creation of a model based on the refrigerant level data and the system data.
    In another characteristic of the method, the model created is also dependent on the hours of the day in which the refrigerant level data and the system data were sampled.
    In another characteristic of the method, the method further comprises performing a linear regression on the system data and the refrigerant level data in order to determine an unmatched linear combination of the system data that estimates the refrigerant level.
    In another characteristic of the method, the method also comprises the determination of an error table having entries corresponding to a difference between the estimated refrigerant level and the refrigerant level data at a particular time of day based on the result of the
    - linear regression. In another feature of the method, the method further comprises the generation of a table that stores an hour effect indicating the amount of effect that an hour on a particular day has on the refrigerant level data for each particular hour of the day. In another characteristic of the method, the method further comprises the execution of an hourly compensated linear regression effect in order to determine a linear combination of the system data and the hourly effect in order to estimate the refrigerant level.
    In another feature of the method, the notification generated in-. hint if the refrigerant was added to the refrigeration system when the difference between the expected refrigerant level and the refrigerant level reading. generator is greater than the upper control limit in a predetermined number of consecutive readings.
    In another feature of the method, the notification generated indicates whether the refrigerant is leaking from the system when the difference between the expected refrigerant level and the refrigerant level reading is less than the lower control limit in a number predetermined number of consecutive readings.
    In another feature of the method, the system data includes an ambient temperature reading, a condenser temperature reading and a discharge pressure reading.
    Other areas of applicability will become apparent from the description provided in this document. The specific description and examples in this Summary are for illustrative purposes only and are not intended to limit the scope of the present invention.
    DRAWINGS The drawings represented in this document are only for the purpose of illustrating selected modalities and not all possible implementations, and are not intended to limit the scope of the present invention.
    Figure 1 illustrates an exemplary refrigerant system;
    .: figure 2 shows an overview of exemplary components of the coolant leak detection system; Figure 3 illustrates an overview of a method performed by the coolant leak detection system; Figure 4 shows a block diagram of the exemplary components used in order to determine a leakage state; figure 5 illustrates an exemplary method for determining a leak condition; figure 6 shows a block diagram of the exemplary components used to erase data; Figure 7 illustrates an exemplary method for erasing data; Figure 8 illustrates a block diagram of the e-“xemplar components to validate data; figure 9 illustrates an exemplary method for validating data; Figure 10 illustrates a block diagram of the exemplary components for selecting a data model; figure 11 illustrates an exemplary method for selecting a data model; Figure 12 illustrates a block diagram of the exemplary components for creating a data model; figure 13 illustrates an exemplary method for creating a model; Figure 14 illustrates a block diagram of the exemplary components of a learning machine; and figure 15 illustrates an exemplary method performed by the learning machine in order to create a data model. Corresponding reference numbers indicate parts corresponding to the different views of the drawings.
    DETAILED DESCRIPTION Exemplary modalities will be described in more detail below with reference to the attached drawings. With reference to figure 1, an exemplary cooling system 100 is shown. The exemplary cooling system 100 includes a plurality
    - compressors 104 channeled together in a common suction pipe 106 and a discharge head 108, all positioned within a compressor rack 110. A discharge outlet from each compressor 104 may include a respective temperature sensor 114. A pressure sensor can be used in addition to, or in place of, temperature sensor 114. An inlet to the suction pipe 106 can include both a pressure sensor 118 and a temperature sensor 120. In addition, a pressure outlet discharge from the discharge head 108 may include an associated pressure sensor 124. As described in more detail, hereinafter several sensors can be implemented for monitoring the. refrigerant level in the cooling system 100. The exemplary compressor rack 110 compresses the refrigerant vapor, which is released to a condenser 126, where the refrigerant vapor is liquefied at high pressure. Condenser 126 may include an associated ambient temperature sensor 128 and an outlet pressure sensor 130. This high-pressure liquid refrigerant can be released to a receiver
    144. The refrigerant from receiver 144 is then released to an evaporator 136. For example, evaporator 136 can be in a food cooler.
    A controller 140 can be used and configured or programmed to control the operation of the cooling system 100. In an exemplary mode, the cooling controller 140 is an Einstein Area Controller, or E2 controller, offered by the company CPC, Inc., of Atlanta, Georgia. It should be appreciated that any other type of programmable controller that can be programmed can also be used. Computer-readable medium 141 is accessible to controller 140 for storing executable code to be executed by the controller
    140.
    The refrigerant level inside the cooling system 100 can be a function of the system load, the ambient temperature, the defrost state, the heat recovery state and the refrigerant charge. A coolant level indicator 142 reads the coolant level
    - inside the cooling system 100 and provides a coolant level output signal (RL). In some embodiments, the refrigerant level indicator Ú 142 is an ultrasonic sensor, which detects the refrigerant level based on an ultrasound beam.
    It is envisaged that other types of sensors, such as flotation sensors or capillary sensors, can also be used as refrigerant level indicators.
    The refrigerant level indicator 142 can be positioned anywhere in the refrigeration system where a refrigerant level can be determined.
    For example, the refrigerant level indicator can be positioned on receiver 144.. A coolant loss detection algorithm uses coolant level data, along with other measured parameters,. such as room temperature (Ta), discharge temperature (Ta) and discharge pressure (P4), in order to determine if there is a refrigerant leak in the refrigeration system 100. In addition, other system parameters - theme can be used to determine if there is a leak in the cooling system.
    The refrigerant leak can be characterized as slow or fast.
    A quick leak is easily recognizable, since the refrigerant level will drop to a predetermined level, for example, zero or approximately zero, for a very short period of time.
    A slow leak, however, can be more difficult to recognize.
    One reason is because the refrigerant levels in the receiver can vary widely over a given day.
    For example, defrost cycles throughout the refrigeration system cause refrigerant levels to vary in the receiver.
    Likewise, changes in room temperature cause refrigerant levels to vary.
    To extract meaningful information, refrigerant levels can be measured and then calculated at predetermined ranges.
    For example, refrigerant levels can be calculated hourly (RLurg). When a refrigerant is not present in the receiver, it may, in this case, be present in capacitor 128. The volume of
    - refrigerant in the condenser is typically proportional to the temperature difference between the ambient air temperature and the temperature in the condenser: pain 128. In general, the refrigerant has a tendency to move to the refrigeration location of the condenser and the receiver in an amount proportional to the temperature difference between the ambient air temperature and the temperature in condenser 128. The loss of refrigerant can be detected, in part, by monitoring these parameters together.
    Figures 2 and 3 illustrate a refrigerant leak detection system and a method for identifying a refrigerant leak, respectively. Referring to figure 2, a block diagram illustrates an overview of the refrigerant leak detection system. Refrigerant leak detection module 7 receives measured data 302, which can include RL level data and system data. System data can include, but is not limited to, ambient temperature (Ta), condenser temperature (Ta) and discharge pressure (Pg). System data and RL level data can be received directly from the sensors or can be retrieved from a measured data database 303 that stores the various sensor data. It is anticipated that leak detection can be done several times in a day, daily or every few days. Thus, the measured data database 303 stores recent RL level data and system data for further analysis. The refrigerant leak detection module 304 can also receive system parameters as inputs. System parameters can be stored in a 306 system parameter data store accessible by the refrigerant leak detection module 304. System parameters can include, but are not limited to, statistical process control (SPC) data and the limits of the SPC control. The - SPC control data and SPC control limits are statistical data relating to the refrigerant level. The refrigerant leak detection module 304 can also access a database of models 305 that stores
    * stores a plurality of data models. The data models can be in the form of a data structure containing samples of previously registered system data data, that is, training data, defining the behavior of a refrigerant level during a certain period. of time. As will be shown, the coolant level can be expressed as a linear combination of the system data, which may or may not be compensated by the hour. However, it is envisaged that other ways of representing the models can be implemented.
    The refrigerant leak detection module 304 can produce. a leak state and / or a notification system to a user, indicating the same. The refrigerant leak detection module 304 uses the 7 measured RL level data, the measured system data, the system parameters and the data models to identify the existence of a refrigerant leak. By using models and system data, the refrigerant leak detection module 304 can determine the expected RL level data, and can determine a leak when the measured RL level data is regularly below the level data Expected RL. If the system is in a leak state, the system can also generate a notification for a technician.
    Figure 3 illustrates a flow chart of a general method that the system can perform in order to identify a leakage state. In step 402, the measured data is read from a controller. As mentioned above, the measured data 302 can be stored in a “measured data data bank 303, or can be received directly from the sensors. The measured data 302 can include Ta, Ta, Pa and RL data. Some or all of the data can be represented as hourly averages, indicating the average values for a particular day (TaHRr, TaHr. Paxr AND RLHR).
    In step 404, the system data can be analyzed in order to obtain a suitable model for analysis in relation to the RL level data. The model can be retrieved from the database
    - 305 models, or can be created in the absence of a suitable model. More details on retrieving and creating models are provided below. In general, however, cooling systems are operated under varying conditions and in different applications. System conditions, such as refrigeration charge, ambient temperature, defrost state, heat recovery state or refrigerant charge model, can influence refrigerant levels and behavior. In addition, the temperature and pressure in the condenser, as well as the daily defrost schedule can also influence the refrigerant level. The models, recovered or created, are defined in order to consider all the relevant factors that have an impact on the levels of refrigerant.
    Once a model is obtained, the system will be able to determine the existence of a leakage state in step 406 using the measured RL level data, the system data and the model obtained. As can be seen from the number of parameters that influence refrigerant levels and the amount of sampling that can occur over time, the received and stored system and RL level data provide rich data sets. The rich data sets allow the system to run several different types of machine learning algorithms and statistical process control methods in order to recognize the existence of a slow leak condition. In step 408, the data and results from steps 402 and 406 are stored and notifications can be generated, if necessary, regarding the status of the refrigerant. In step 410, the system waits for a predetermined amount of time before operating again. The system can continue to monitor the various sensor data and store the sensor data for further analysis. It is anticipated that the system can be operated daily, but can operate more or less frequently. Figure 4 illustrates in more detail an exemplary embodiment of the refrigerant leak detection module 304. As mentioned, the refrigerant leak detection module 304 receives the system data and the RL level data as an input. A data cleaning module
    . 504 can be configured to receive raw system data and RL level data and can process system and RL level data from | so that the refrigerant leak detection module 304 can analyze and process the measured data. In addition, the data cleaning module 504 can discard any data that it determines to be unreliable. Raw data can be pre-processed so that it is at least calculated every hour. In some modalities, each hour of the day will have an average hour corresponding to the time of day. The model 508 module can communicate with the system parameter database 306 in order to receive data and limits. SPC control, as well as model data. The model module 508 can retrieve a stored data model from a plurality. of data models from the model database 305 or you can create a data model for storage, in case a suitable model does not exist. The terms "model" and "data model" can be used interchangeably.
    The clean data and acquired data models communicate with an SPC 504 control module. The SPC 504 control module furthermore uses SPC control data, including (the mean) Xrar, (the range) R , the upper control limits (UCL) and the lower control limits (LCL) in order to determine a refrigerant level error state. SPC control data and limits can be calculated from previously registered system data and measured refrigerant level data. In addition, each model can have its own data and SPC control limits.
    Xpar It is an average error value between the current refrigerant level hourly averages and the expected refrigerant level hourly averages over a predetermined period of hours. The average Xra; can be calculated as follows: 2 (Ria, “RE;) tw O in which RLmi is the expected average hour of hour i, RLravei is the average of
    , hour of hour |, and where n is the number of hours in a subgroup. Read the range of error values over the predetermined number of hours. Since the mean Xp, and the range R are calculated for the subgroups, the standard deviation can be calculated according to the following: S & Xu) EO 2) n where n is the total number of instances used to calculate the mean and in which x; is the refrigerant level reading in instance i. Using the standard deviation and the mean, the UCL limit can be set to Xrpar + 3rd and the LCL limit can be set to Xpar - 30. '10 The SPC 504 control module may produce an error indicating. that the refrigerant level is too high (runOve- rUCLError), too low (runUnderLCLError), or in a leaking state (runUnderMeanError). Further details on the calculation of the SPC control data, and subsequent error indications are provided in more detail below.
    Figure 5 illustrates an exemplary process that can be performed by the refrigerant leak detection module 304. In step 602, level data RL and system data Ta, Ta, € Pg are received and can be processed by data cleaning module 504. As mentioned —nothing, RL level data can be received as raw data from refrigerant level indicator 142. Sensors can provide unreliable or "bad" data over time. Thus, the data cleaning module 402 can identify which readings are unreliable and remove those readings from the RL level data and / or system data. The details of data cleaning are provided in more detail below.
    In step 604, the cleaned data can be stored in the measured data database 303 or can be communicated directly to the SPC 512 control module. In step 606, the system accesses the data for a given day for detection analysis escape. The system cannot be run continuously, so data from a previous day or a previous sensor reading can be analyzed, as opposed to
    . to the most recently measured data.
    In step 608, the data corresponding to the day being analyzed are used to select an appropriate model. The model module 608 can first try to choose a stored model from the system parameter data store using the system data. As will be described, the models define the behavior of the refrigerant type based on various data, including system data, Ta, Ta, E Pa. In step 610, the selected model is evaluated in order to determine whether the model is suitable. In order to determine the reliability of a model, the absolute error value of the model in relation to the training data, for example, the previously stored data, can be compared with a predetermined limit, for example, 50% . When the value 7 of absolute error of the model is less than the predetermined limit, the model can be considered adequate. When the model is suitable, the process moves to step 618, or still to step 612.
    In step 618, the selected data model and RL level data are analyzed. RL level data is entered into the data model in order to generate an expected RL level, or RLmv. The analysis result is used to generate the SPC control data as well as the & SPC control errors. In step 620, the SPC control errors are analyzed. The analysis may include the generation of SPC control graphs using known graphical SPC control algorithms. The SPC control algorithm can create (average) Xrar and (range) R graphs for both RLy and RLurg data. For each hour, the RLyw data can be compared with the RLianr data in order to determine a refrigerant error level RLerror- For example, for the hourly data, the error can be calculated as RLerror = RLm - RLaHr. RLaHr data can be divided into subgroups. For example, if the data are hourly, as in the present case, and there are 24 hours of data, three subgroups of eight hours per piece can be generated for each day. For each subgroup, the hourly averages of the RLerror data can be calculated. The result of this average is the Xva value, for the subgroup. The RLeror data range is also calculated in order to determine R. Es-
    * These graphs can then be compared to determine whether RLeror data falls within the upper control limits (UCL) and the lower control limits (LCL) for RLv.
    As described above, the UCL and LCL limits can be calculated as three standard deviations plus and minus Xra.
    An SPC control counter is incremented with each consecutive time in which the RL level data falls outside the UCL or LCL limits.
    Based on the value of the SPC control counter, the existence of an error can be determined.
    For example, when RLhg data is measured below the LCL limit in seven consecutive readings, a deerro notification, runUnderlCLError, can be generated.
    When the RLuhr data is: measured above the UCL limit in seven consecutive readings, an error notification, runOverL CL Error, can be generated.
    When the calculated Xpar average is continuously decreasing for seven iterations, in this case, a runUnderMeanError notification can be generated.
    Other variations of notification-generating rules can be implemented.
    For example, when a number less than seven is selected, the system can identify more errors.
    In contrast, when selecting a number greater than seven, the robustness of the error identification would become greater, but at the expense of the correct identification of errors.
    In addition, it is anticipated that an error can be identified when the error conditions are substantially consecutive.
    For example, when six of the seven readings are above the UCL limit, a runOverLCLError notification could still be generated.
    In step 620, SPC control error notifications are interpreted.
    A runOverUCLError notification can indicate whether a refrigerant has been added to the refrigeration system.
    Technically, the ruNOverUCLError notification may not be an error in itself, but it can also indicate whether the refrigerant has been added to the system.
    The system can create a notification that the refrigerant has been added to the system.
    A runUnderLCLError notification or a runUnderMeanError notification indicates that the refrigerant is leaking from the refrigeration system.
    The algorithm can create a refrigerant leak notification.
    As mentioned, when in step 610, an adequate model
    * when it has not been recovered, the process moves to step 612. In step 614, the system can determine whether the reason for the model's refusal was due to a very large variation in the model. If positive, then a notification is generated , thereby notifying a technician that the model creation methods may need refinement.
    In step 616, the system determines whether there is more data to be analyzed, that is, whether more RL-level data needs to be analyzed. If this is the case, the system moves to the next day of the RL level data to be analyzed in step 622. If not, the system moves to step 624, in which results are collected and stored. It is expected that. notifications generated in step 618 and interpreted in step 620 can be stored in an error database or communicated to a technician. In addition, the system can include visual or audible indicators to warn operators or technicians of the condition of the refrigerant in the refrigerant system.
    The following sections describe various modules and details that can be implemented in the refrigerant leak detection system. The examples provided are in no way intended to be limiting, but rather provide greater detail of the possible modalities of the refrigerant leak detection system.
    As mentioned above, in some modalities, the measured data may need to be cleaned. Figures 6 and 7 illustrate a data cleaning module and a method that can be performed by the data cleaning module, respectively. Next, with reference to figure 6, an exemplary data cleaning module 504 is illustrated. The data cleaning module 504 receives the measured RL level data and the measured system data, TaHr, TaHr, € Par. However, it is anticipated that the data cleaning module 504 can receive the raw system data, for example, Data Ta, Ta and Pa can calculate the hourly averages of the same. The cleaning data from module 504 can also receive the previously stored data 706. The stored data 706 can include the existing data in a predetermined data structure, organized by date. O
    . data cleaning module 504 can generate a data table 708, which can include the clean time data RLur, TaHr. TaHr & Parr Organized by day. Figure 7 illustrates an exemplary method for cleaning data. The following method can be performed by the data cleaning module 504 described above. It should be noted that certain types of sensors, such as ultrasonic sensors, may still require data cleaning, unlike other types of sensors, such as flotation sensors, which cannot generate data that require cleaning. In step 802, the measured RL level data and the system data are grouped by date. 'In step 804.0s, RL level data for a given date can be retrieved. Step 806 can determine whether the data is raw data from 1 sensor or is in a time data format. If the data is in the RLanr hourly average format, the data is determined to be cleared and added to the system data for the day Taxr, TaHr AND ParR in step 818.
    When the data is raw data from a sensor, step 808 can check the sensor data to determine if it is reliable and can create notifications indicating whether the sensor is working correctly. Further details on determining the reliability of the data are provided below. Step 810 can skip the filtering and calculation step, if step 808 determines that the sensor data is not "credible". In this situation, this untrusted data is discarded and the system retrieves the next data set. When the data is determined to be reliable, step 820 will be able to determine if there are more days to process, and if that is the case, step 822 will be able to select the next day for the data to be cleared in step 804. The algorithm can run until there is no more RL data to be cleared.
    Figure 8 illustrates exemplary components of the data cleaning module, which verify the reliability of the measured RL level data. A sensor validation module 904 receives RL level data. The data can be represented in an hourly average format or in a
    - raw format. The 904 sensor validation module can perform one or more of an empty filter test, a bad filter test, an misaligned filter test and a good filter test. The filter tests can be stored as executable instructions in a computer-readable memory associated with the 906 system. A 908 sensor barometer module can access a 910 sensor barometer, based on the results of the filter tests. The sensor barometer 910 can be a counter that has its value modified based on current and old sensor readings. For example, in an exemplary mode, the counter can be incremented after each time the process determines the sensor data. are reliable, and decremented when the sensor data is determined to be unreliable.
    ] Figure 9 provides the steps for determining whether the sensor data is reliable. As shown above, the system may not need a validation of the sensor data, depending on the type of sensor. An ultrasonic sensor may involve data validation, but other types of sensors do not. As mentioned, RL-level data can be executed through filters incorporated as machine-executable instructions stored in a computer-readable medium. Filters can include, but are not limited to: an empty filter 1002, a bad filter 1008, a misaligned filter 1014 and a good filter 1020. It should be understood that filters can be run in any order, or need not be run in the order described above. Filters can be based on extracting characteristics from RL level data and applying results-based rules.
    The empty filter 1002 can be any filter that determines whether the sensor data is empty or substantially empty. For example, in one embodiment, empty filter 1002 can process RL-level data in order to determine the number of data samples with a value, that is, a y value, less than a predetermined value, for example, 2. When the percentage of data samples with a y value, less than the predetermined value, is greater than a predetermined percentage limit, for
    - example, 50%, the RL level data can be considered empty in step 1004 and marked as empty in step 1006. After step 1006, the 'sensor barometer can be decremented in step 1026. When the level data RL are not determined to be empty, the algorithm can proceed to the bad filter 1008. Next, an exemplary pseudo-code algorithm is provided that can be executed on the empty filter: Start: total points = O; empty points = O; i = 1; . while (RL;! = EMPTY) if (RL; <predetermined value) "empty points = empty points + 1; total points = total points + 1; if ((empty points or total points) <percentage limit) RL = empty; sensor barometer = sensor barometer -2; otherwise go to Bad Filter (); Bad filter 1008 can be any filter that determines whether the sensor data is unreliable.
    For example, an exemplary bad filter 1008 detects whether the hourly RL level data is in an almost straight aligned state.
    An almost straight aligned state can be considered as a state in which a curve that represents the RL hour level data is linear, or almost linear.
    Filter 1008 selects a starting point to evaluate a portion of the data.
    Filter 1008 designates the starting point, either at the first point of the portion of the curve being analyzed or at the end point of the previous almost straight line.
    The starting point is stored and the next point on the curve is read from the table that stores the measured RL level data.
    When the difference between the y value of the next point and the y value of the first point is greater than a predetermined limit, for example, e = 0.3, in this case the next point is the end point of the
    - current line.
    When the difference in the y value is less than e, the scanning process is repeated until a difference in the y value between a next point and the first point is greater than and for the end point of the current line.
    For each almost straight line, the length and average of the y-value of the line can be calculated.
    When the length of the almost straight line is greater than a length limit, for example, a = 20, and the average y value is greater than a limit of average value, for example, b = 10, in this case the almost straight line can be classified as a qualified line.
    When the number of qualifying lines in a data set exceeds a cumulative line limit, for example, c = 3, or when the number of points en-. on all qualifying lines exceeds a cumulative length limit, for example, d = 60, in this case the data represented by. curve are classified as unreliable.
    When the RL level data is reliable, in step 1010, the data is marked as unreliable in step 1012 and the sensor barometer is decremented in step 1026. When the RL level data is unreliable, the algorithm remains in the misaligned filter 1014. Next, a pseudocode algorithm is provided that can be performed on Bad Filter 1008. Start: i = 2; start = 1 qualified lines = O; cumulative length = O; For all data points on the RL contour curve: RLtot = RLetart length = O; While (| Rstart - RLi | <e) length = length + 1; isi + 1; RLtot = RLtot + RL ;; if (length> 20 && ((((RLto: / (i-start))> b) increment qualified lines;
    . cumulative length = cumulative length + length; ] start =; i = i + 1; if (qualified length> c OR cumulative length> d) curve RL = bad; sensor barometer = sensor barometer - 2; otherwise go to Misaligned Filter () The 1014 misaligned filter can be any filter you determine. when the sensor is misaligned.
    For example, a misaligned filter and example 1014 deals with six cases that can lead to the conclusion that the filter is misaligned.
    A first case can analyze the RL level data in order to determine if the curve has a lot of variation of RL level values, that is, y values, from point to point.
    An absolute difference value between the y-values can be calculated for each pair of adjacent data points.
    For example, the following equation can be used: DV; j + 1 = IRLi - RLis1] An average of the calculated absolute difference values can then be calculated using, for example, the equation: DV., = DV, 3 + DV, + ust DV, ia HDV, 1, n in which, DVaw, is the mean difference value and DV;. + 1 is the absolute difference between the RL level value of reading ij and reading (i + 1), and when there are n total readings.
    Each calculated absolute difference value is then compared with the sum of the average absolute difference value and a predetermined value, for example, k = 15, and a counter can be incremented for each case in which the absolute difference value exceeds the value of the average absolute difference.
    The process will then determine whether the amount of difference values greater than the sum of the average difference value and the default value is too high.
    One way to determine whether this condition exists
    . te is by multiplying the number of data points, n, by a second predetermined value, for example, b = 2. The following represents pseudocode i for a possible implementation of the previous case: Start CASE 1: Calculate all DV values; Calculate DVawg; To (all DV values) SE (DVii1> DVavg + k) increase the counter; . if (counter> n * b)> curve = misaligned; 'decremented sensor barometer; otherwise go to case 2; In a second case, the misaligned filters 1014 examine whether a percentage of the points with a y value below a limit, for example 2, is greater than a percentage limit, for example, 55%. If so, the curve is misaligned.
    Otherwise, the filter moves on to the next case.
    In a third case, the exemplary misaligned filter 1014 determines whether a certain percentage of points falls within the qualified lines.
    The determination is similar to that of the bad filter 1008, but the parameters can be adjusted, and the behavior of the curve that constitutes an misaligned curve is slightly different.
    The third case of the misaligned filter 1014 labels a segment of data points as a qualified line when the length is greater than a small qualified line limit, for example 5. The third case will then calculate the number of points of data falling on the qualifying rows versus the total number of data points on the data curve.
    When the percentage of points falling on the qualified lines is greater than a percentage limit, for example, 55%, in this case the curve is labeled as misaligned.
    In a fourth case, the misaligned filter 1014 examines vectors
    , well extrapolated from the original data curve. Since the well vector is extrapolated / generated from the original data curve, the points of the well vector are analyzed in relation to the neighboring points on the well vector. An analysis similar to that of the third filter is made in such a way that the misaligned filter 1014 determines how many points in the well vector fall within the qualified lines. When the number of points in the well vector that fall within the qualified lines versus the number of points in the well vector exceeds a percentage limit of the well vector, for example, 40%, in this case the data curve is labeled as misaligned . It is anticipated that the length of a qualified length in the fourth case need not be. equal to the length of the qualified line of the third case. For example, in the third case, a qualified line is characterized as having a length of five points, the fourth case can label a qualified line in the well vector as covering four points.
    In a fifth case, misaligned filter 1014 examines the peak vector extrapolated from the original data curve. The steps performed in the analysis of the peak vector are essentially the same as the steps performed to analyze the well vector. That is, the misaligned filter 1014 will extrapolate / generate a peak vector from the data curve. When the peak vector is extrapolated / generated from the original data curve, the points of the peak vector are analyzed in relation to the neighboring points on the peak vector. An analysis similar to that of the third and fourth filters is made, in such a way that the misaligned filter 1014 determines how many points in the peak vector fall within the qualified lines. When the number of points in the peak vector that fall within the qualified lines versus the number of points in the peak vector exceeds a percentage limit of the peak vector, for example, 50%, in this case the data curve is labeled as misaligned. It is anticipated that the length of a qualified length in the fifth case need not be equal to the length of the qualified line in the third or fourth case. For example, in the third case, a qualified line was characterized as having a length of five points and, in the fourth case, the length of the qualified line was four points. In the fifth case,
    . a qualifying line can be three points long.
    In the sixth case, the distribution of the points on the data curve is' analyzed. The sixth case of the exemplary misaligned filter 1014 determines whether many data points are within a small range, thus indicating misaligned data. The misaligned filter 1014 will separate the data curve into segments of equal length, for example, three units per segment, referred to as ranges. The misaligned filter 1014 will, in this case, calculate the number of points that fall within each range. The range having the most points is labeled LRange. A stitch counter will be adjusted to the number of stitches falling into the LRange range and one. track counter will be set to one. The misaligned filter 1014 will then analyze the bands against each other. . The following will be performed iteratively until the point counter is greater than or equal to 60% of the total number of points on the curve or until there is no more range to analyze. The misaligned filter will compare the LRange range with the next most populous range, labeled NRange. When the number of points in the NRange range is equal to or greater than 20% of the number of points in the LRange range, in this case, the point counter will be increased by the number of points in the NRange range and the range counter is increased. Or, yet, no counter is incremented. The LRange range, in this case, is defined as equal to the NRange range, and the NRange range becomes the next most populous range. This cycle will be repeated until there are no more ranges to analyze or the point counter is greater than or equal to 60% of the total points in the curve.
    After leaving the circuit, the misaligned filter will check if i) the point counter is greater than or equal to 60% of the total number of points on the curve, and ii) the value of the range counter is greater than or equal to a range limit, for example 2. If both conditions i) and ii) are false, in this case, the misaligned filter 1014 classifies the data as misaligned and unreliable. The sensor barometer can also be lowered. When the misaligned filter 1014 discovers that the sensor is
    , misaligned, in step 1016, the Misaligned Filter 1014 may indicate whether the sensor is misaligned or if the RL level data curve can be marked as misaligned in step 1018. Otherwise, the sequence may continue for the Good Filter 1020 When the Good Filter 1020 concludes that the RL level data is good, step 1022 may indicate that the data is good and the RL level data curve may be marked as good at step 1024. When the data is not good, step 1022 may indicate that the data is not good and the RL level data curve may be marked as unknown in step 1024. In some embodiments, a 908 sensor barometer can: be used as a means of determining reliability sensor data.
    When the reading on the sensor barometer 308 exceeds a certain limit, the data is determined to be reliable, or the data is considered to be unreliable.
    As described above, when one of the data filters determines a state of the data curve, the counter can be increased or decreased based on the state.
    If, after filtering, the data is determined to be reliable, and step 1030 is reached, the barometer may be increased by an integer value, for example, 1. When the filters determine that the data is empty, bad or misaligned, and step 1026 is reached, the barometer can be decreased by an integer value, such as 2. The sensor barometer can be initially set to a predetermined value, for example, 4, and have a bad value - maximum, for example 8. The barometer can be used to indicate whether the data has reached a warning limit, for example, 4, or if an alarm limit has been reached, for example, 1. With these exemplary predetermined values, two days consecutive decrements may result in a warning, and 4 consecutive days may always result in an alarm.
    When the barometer is incremented in step 1030, the next step can check whether the barometer is in a warning state.
    If so, the return value with respect to whether the data should be used can be set to not OK in step 1036. Only after a sufficient number of good sensor readings are received, in order to remove the state of
    . warning by the data, the return value will be set to OK in step 1034. When the barometer is decreased, the return value can be set to not OK in step 1036. In step 1038, it can be determined whether the barometer is in an alarm state. If so, an alarm notification is generated at step 1040.
    The following explains the exemplary components and methods for selecting and creating models. The previously measured data can be used as training data in order to create the data models. Training can be supervised or unsupervised.
    Next, with reference to figures 10 and 11, the selection of models, of an existing model from the model database 305 is illustrated. In figure 10, the data to be analyzed 1102, including data from RLur, TaHr, TaHr, E Par, can be received or accessed from the measured data database 303 by the existing model module.
    1104. Model data 1106 can be accessed from the model database 305 through the existing model module 1104 and can include existing model data and existing model hypercube boundary data. The hypercube limit data is compared with the system data measured by the existing model module 1104 to determine whether the corresponding model is a match. The existing model module 1106 can use the data to be analyzed 1102, for example, the measured data, and the model data 1106 to create a selected model 1108. The production of a selected model is the refrigerant level expected, RLm. The RLy level can be compared with the RLur data by the SPC control module, described above, in order to determine a leak state.
    With reference to figure 11, the steps describe a method for selecting an existing model using the data corresponding to a particular day. In step 1202, an existing model can be accessed. Each model can have a defined hypercube limit for comparison with the time of day data RLHr, Tanr, TaHr. It's pair. The hypercube can define the limits of Taxr, TaHr, E Par, so that a suitable model
    'can be selected. For example, the hypercube can include data representing TarieH, TarieH, & PaxieH € Tarow, Tarow, E Parow. In step 1204, the system can calculate the percentage of the hour data, Taxr, TaHr AND ParR, which are within the hypercube limit of the existing selected model. In step 1206.0 the system can determine whether the percentage of hourly data that falls within the hypercube limit is greater than a predetermined percentage, for example, 80%. When the percentage of time data that falls within the hypercube limit does not exceed the predetermined limit, in step 1210, it is determined whether there are more existing models to be selected, and, if there are more models to be selected, step 1212 can: move to the next model. This can continue until all models are tested, that is, step 1210 = no, or a valid model is' found, that is, step 1206 = yes. When a valid model is found, the valid model can be adjusted to the selected model existing in step 1208. In step 1214, the system determines whether an existing model has been selected. If so, the model selection process is complete. Otherwise, the steps described in figure 13 can be performed by the components in figure 12 in order to select a model for each data set.
    Figures 12 and 13 illustrate the components and the method used to generate a model, respectively. As presented, a model will be generated when there is no reliable pre-existing model with the measured system data and hypercube data from the plurality of data models.
    In figure 12, a model 1308 configuration module communicates with a model 1312 barometer module and a data creation module 1306. The data creation module 1306 receives the data to be analyzed and the data previously recorded, and updates the training data by attaching the data to be analyzed from the registered data to the model data. When using the updated data, that is, the previously registered data and the data to be analyzed, a new model is created by the model configuration module 1308. The
    'model 1306 configuration accesses various machine learning procedures incorporated as computer executable instructions in the computer readable medium.
    The production of the 1308 model configuration module is a new model.
    A model barometer can be incremented each time a valid model is used and decremented each time an invalid model is created.
    When the model barometer reaches a predetermined model barometer limit, the model 1312 barometer module will determine if the models have a lot of variance and generate a notification.
    Further details on model creation are provided below. - Figure 13 illustrates an exemplary process that can be performed by the leak detection system in order to create a new model.
    In step 1416, the measured data for the day to be attached are defined in order to be analyzed.
    In step 1418, the process checks whether the day index of the data to be changed is a valid data set, and if not, the process moves to step 1426. When the day index is valid, the process moves to step 1420, which determines the number of RL-level entries in the model creation data.
    In an exemplary mode, 3-day data, or 72 hours of data, can be used to create a model.
    In step 1420, the process checks if the number of non-NULL entries in the model creation data is less than 72 (or 3 days of entries), if not, the process moves to step 1426. When the creation data models have less than 72 hours of data, in this case the current date data, including TaHr, Tear, and Par are changed to model creation data.
    The index counter is incremented in step 1424. This portion of the process is repeated until there are more than 72 valid RL-level entries in the model creation data, or until there are no valid data to be attached.
    In step 1426, the process determines whether there is enough data to create a model.
    If there is not enough data, the process ends and no model is created.
    If there is enough data, however, the process moves to step 1428, at which point a new model is created using, for example,
    'example, an hourly compensated linear regression.
    Production is a new model. i The new model is then validated in step 1430. When the model is valid, in this case, it will be adjusted to the model selected in step 1432, stored in the model database 305 in step 1442, and the model barometer is incremented.
    When the model is not valid, the model barometer is decremented in step 1436. The model barometer is then read in order to determine if it is in an alarm state, that is, if the reading barometer is less than the predetermined model barometer limit.
    When the ba-. model rometer is in an alarm state, in this case, a very large variance in the data is assumed.
    Further details on the creation of the model in question are provided below.
    Figures 14 and 15 illustrate the components and the method used to build a model.
    This modeling system behavior approach assumes a defrost programming programmed in a commercial refrigeration system.
    Other approaches may be used, depending on the type of cooling system, for example, hourly compensation may not be necessary if the type of cooling system does not include a defrost schedule.
    A time effect can be learned, which is to be a proxy service for the change load profile.
    It is also possible to calculate the load with more sensors through a balance of mass and energy.
    When the load is calculated, the current and previous hour load can be used with Taur, TaHr, PaxR data with a variety of learning machines, such as linear regression, neural networks, M5 and similar methods.
    Although the present invention, in general, uses units of hours, it must be recognized that the steps described or illustrated may use different time intervals.
    It should be recognized that a number of classifiers, such as the linear regression learning machines used in the present invention, can be applied.
    It should also be recognized that a variety of clustering algorithms, such as
    'as those of K-averages can be applied. In figure 14, an hourly compensated linear regression machine 1504 can access model creation data 1502, including RLur, TaHr, Tara, E Paxr data from a computer-readable medium. Data transformation procedures for data store 1506 may include, but are not limited to, multiple linear regression, grouping by K-averages and time-effect algorithms, which can also be accessed from a computer-readable medium. The hourly compensated linear regression learning machine module 1504 can produce new data from model 1508, including the new model and no. limits on models.
    Figure 15 illustrates the measures taken to create a 'model. In general, the steps include the execution of a classification algorithm on the training data, for example, the data previously registered, in the step. In step 1602, an error table is constructed using the classification results of step 1604, a clustering algorithm is performed on the training data in step 1606, the effect of the time of day is determined in step 1608, and a second classification algorithm is performed in step 1610, the second classification algorithm takes into account the time of day. These steps result in a learning machine capable of learning the effect of the day.
    In an exemplary modality, the model creation data is analyzed by a multiple linear regression learning machine in step 1602. The multiple linear regression learning machine — performs a linear regression using the measured RL level data and the measured system data. An exemplary linear regression attempts to choose the ideal weights to express the output value. Therefore, in this order, a linear regression can be used to find the ideal weights, such that the RL level is expressed as a combination | i- —nearde TaHr, TaHr, E Par. In this way, the RL level can be written in the following format: RL = Wo + W1as + W2A2 + W3a3z (3)
    . where a1 is Taur, a2 is Taxr; and a3 is Par.
    The previously collected data, that is, the training data are used in order to optimize the weight selection.
    Each instance of data, that is, RL level data and system data belonging to a particular time, is represented according to equation (3). In this way, the first instance can be represented as: RL = wo + wia'1 + woa '/ o + waa's (a) or> bv, a!) ”P (5). 10 in that, is equal to 1. The expression above is an expression that can be used to predict the level of refrigerant fluid, considering the. system data.
    The difference between the predicted values and the current values of the refrigerant level can be used in order to optimize the selection of weights.
    The objective of the regression is to minimize the error for the entire training data set.
    Thus, if there are n instances in the training data, the sum of the squares of the differences can be represented by the following expression: n: 3 i if Dr (6) in which i is the instance and x is the current refrigerant level for that instance.
    When selecting the coefficients wo ... W «that minimize the error, a model can be defined in order to better predict the refrigerant level.
    It is anticipated that other regression techniques can be performed.
    In addition, it is anticipated that other types of classification algorithms can be used in place of a regression.
    The above was provided as an example of a linear regression.
    In step 1604, an error table is constructed using the results of the regression, that is, the model, and the measured RL level data and system data.
    For each hour, the system data is run through the model in order to determine an expected refrigerant level, that is, RLw.
    For each hour, the error table can be filled in with the
    * refrigerant level error, ie RLw - RLanr.
    Therefore, the final result can be an error table, with 25 columns and 24 rows, one column for each hour of the day, in which each row corresponds to a different time.
    For example, the 25th column is the data for a given day.
    The 25th column can include a number of errors for the hour corresponding to the row.
    In addition, the error values can be normalized between -1 and 1. The following is an example of two lines of an error table:: 10 As can be seen from the table, at hour 1, a normalized error of 0.25 was measured, and at hour 2, a normalized error of -0.25 was measured.
    When representing the table in the following format, the Euclidean distance between each hour of the day is equal.
    In step 1606, a clustering algorithm can be performed on the table of error values.
    In some modalities, the grouping algorithm is a grouping by k-means.
    The first step in the cluster is the definition of how many clusters to look for, that is, k.
    Although not necessary, 4 to 8 groupings can be used.
    Then, points k can be chosen at random as the centers of the grouping.
    In this case, for each instance, the Euclidean distance from each cluster center can be calculated, and each instance is assigned to the closest group.
    When all points are assigned to a grouping, a new center is chosen, and the data is run grouped again.
    This step can be repeated until two consecutive turns are made, in which all or substantially all instances of data are assigned to the same grouping.
    When applying this concept to the task in question, each hour may have a distribution of errors associated with it.
    The k-means grouping algorithm can group the samples, that is, the hours, by means of their associated error distributions.
    The result of this group
    'ment will be a map in which the keys are the hours and the entries corresponding to the keys are the sets that have a sum of the distributions for the hour and the grouping to which the hour belongs. In this case, for each hour of the day, a vector is constructed, which vector contains the average distribution for the cluster at that time of day. Then, a list of hours can be constructed based on the grouping to which the hour belongs. In this way, each hour is assigned to its closest grouping based on the average hourly distributions. The results of the second cluster are a second map with k clusters as keys, and the map entries are the hours of the day to which the corresponding cluster belongs. Thus, when there are seven clusters, the first clusters can have, for example, 1, 4 and 11 hours pertaining to them. Thus, at the end of the clusters, each hour of the day will belong to one of clusters k. The groupings are based on the error distributions corresponding to the time of day.
    In step 1608, the effect of the time of day will be determined based on the results of the grouping. For each cluster, the error distributions of the time corresponding to the cluster will be analyzed. For each cluster, all calculated errors are averaged.
    Thus, from the example above, errors for hours 1, 4 and 11 can be averaged together. Then, a new map is built, which is a time effect map. The map keys are the groupings and the entries corresponding to the keys are the average error of the refrigerant level for the corresponding group. The average error for each cluster can then be normalized. From the resulting standardized map, a time effect table can be constructed. The time effect table is indexed by the time of day, and the value corresponding to the time of day is the normalized error effect for that particular hour, which corresponds to the grouping to which the time of day belongs. The following is an example of a portion of the time effect table, keeping in mind that hours 1 and 4 belong to the same group:
    'Time Effect o1 -0.3 02 0.4 o3 0.2 04 -0.3 The error effect table can be used in hourly compensated linear regression, which is performed in step 1610. In regression, data Model creation data for each hour can be treated as an instance.
    Again, with reference to equation (3), the time of day effect can be incorporated into the linear combination.
    Therefore, the level readings. Expected RL can be expressed as the equation: RL = wo + Wia1 + W2a2 + W3A3 + WaAa "in which a4 is the hour effect for a particular hour and w4 is the weight of the error effect.
    Similar to what was described above, the expected RL level can be represented by the expression: bra) = The above expression is an expression that can be used to predict the level of refrigerant, considering the system data and the time of day effect.
    The difference between the predicted values and the current values of the refrigerant level can be used in order to optimize the selection of weights.
    Therefore, if there are n instances in the training data, the sum of the squares of the differences can be represented by the following expression: n 4 t sf. va) = the one in which there is the current refrigerant level for that instance.
    By selecting the coefficients wo ... w «that minimize the error, a model can be defined in order to better predict the refrigerant level.
    It is anticipated that other regression techniques can be performed that take into account the additional hour of the day effect.
    The result of the regression is an hourly compensated data model.
    . In step 1612, the limits of a valid hypercube space for the model can be calculated. The various system data used to create the model are statistically analyzed in order to determine an average and standard deviation. In this way, the hypercube limits can be defined as the narrowest of the range and the average standard deviations plus or minus 3 for each input resource TaxHr, Taxr E Parr- When the hypercube limits are added to the model, model creation can be completed.
    Attached to this document as an appendix is a sample source code used to perform the mo- creation. delo.
    As used herein, the term "module" 7] can refer to, be part of, or include an Application Specific Integrated Circuit (ASIC); an electronic circuit; a combinational logic circuit; a field programmable door matrix (FPGA); a processor (shared, dedicated, or group) that executes the code; other suitable components that provide the described functionality; or a combination of some or all of the above, such as on a chip system. The term "module" can include a memory (shared, dedicated, or in a group) that stores the code executed by the processor.
    The term "code", as used above, can include software, firmware and / or micro code, and can refer to programs, routines, functions, classes and / or objects. The term "shared", as used above, means that some or all code from multiple modules can be executed using a single (shared) processor. In addition, some or all codes from multiple modules can be stored by a single (shared) memory. The term "in a group", as used above, means that some or all of the codes in a single module can be executed using a group of processors. In addition, some or all codes of a single module can be stored using a group of memories.
    The apparatus and methods described in this document may
    'must be run by one or more computer programs run by one or more processors.
    Computer programs include executable instructions on a processor that are stored in a non-transitory, tangible computer-readable medium.
    Computer programs can also include stored data.
    Non-limiting examples of the non-transitory tangible computer readable medium are non-volatile memory, a magnetic storage, or an optical storage.
    The present description is merely exemplary in nature, and therefore variations should not be considered as a departure from the spirit or scope of the teachings. . The above description of the modalities has been provided for purposes of illustration and description.
    It is not intended to be exhaustive or to limit this invention.
    The individual elements or aspects of a particular modality, in general, are not limited to that particular modality, but, when applicable, are interchangeable and can be used in a selected modality, even if not specifically shown. or described.
    These elements can also be varied in many ways.
    Such variations should not be considered a departure from the present invention, and therefore, all modifications are designed to be included within the scope of the present invention.
    权利要求:
    Claims (20)
    [1]
    1. System for detecting refrigerant leakage in a refrigeration system, comprising: - a refrigerant level sensor that detects a refrigerant level in the refrigeration system and generates refrigerant level data based on the level of soft drink; - a plurality of system sensors that detect the conditions corresponding to the cooling system and generate system data based on the detected conditions; - a model database that stores a plurality - of models that define expected refrigerant levels based on previously registered system data, where each model has an upper control limit and a lower control limit associated with them; - a model selection module that selects a model from the model database based on the system data and previously registered system data; - a refrigerant level prediction module that generates an expected refrigerant level based on the system data and the selected model, and - a notification module that generates a notification when a difference between the expected refrigerant level and the reading of refrigerant level is greater than an upper control limit and less than a lower control limit on at least one consecutive reading.
    [2]
    2. System according to claim 2, further comprising a model creation module that creates a model based on the refrigerant level data and the system data.
    [3]
    3. System, according to claim 2, in which the model created by the model creation module is also dependent on the hours of the day on which the refrigerant level data and the system data were sampled.
    [4]
    4. System according to claim 2, in which the module
    'model creation performs a linear regression in order to determine a non-compensated linear combination of the system data that estimates the refrigerant level.
    [5]
    5. System according to claim 4, in which the results of the linear regression are used in order to determine an error table having entries corresponding to a difference between the estimated refrigerant level and the refrigerant level data at a particular time of day.
    [6]
    6. System, according to claim 5, in which the model creation module generates a table that stores an individual time effect. cating the amount of effect that an hour of the day has on the refrigerant level data for each hour of the day. '
    [7]
    7. System according to claim 6, in which the model creation module performs an hourly compensated linear regression effect to determine a linear combination of system data and the hourly effect to estimate the level of refrigerant.
    [8]
    8. System according to claim 1, in which the notification module generates a notification that the refrigerant has been added from the system when the difference between the expected refrigerant level and the refrigerant level consumption is greater than the limit control over a predetermined number of consecutive readings.
    [9]
    9. System according to claim 1, in which the notification module generates a notification that the refrigerant is leaking from the system when the difference between the expected refrigerant level and the refrigerant level consumption is less than the limit lower control in a predetermined number of consecutive readings.
    [10]
    10. The system of claim 1, wherein the system data includes an ambient temperature reading, a condenser temperature reading and a discharge pressure reading.
    [11]
    11. Method for detecting refrigerant leakage in a refrigeration system, comprising: - detecting a refrigerant level in the refrigeration system;
    '- generate refrigerant level data based on the refrigerant level: generator; - detect the conditions corresponding to the cooling system; - generate system data based on the conditions detected; - store, in a model database, a plurality of models that define the expected refrigerant levels based on previously registered system data, in which each model has an upper control limit and a control limit lower control associated with the same; . - select a model from the model database based on the system data and previously registered system data; - generate an expected refrigerant level based on the system data and the selected model; and - generate a notification when a difference between the expected refrigerant level and the refrigerant level reading is greater than an upper control limit and less than a lower control limit in at least one consecutive reading.
    [12]
    12. Method according to claim 11, further comprising the step of creating a model based on the refrigerant level data and the system data.
    [13]
    13. Method, according to claim 12, in which the model created is still dependent on the hours of the day in which the refrigerant level data and the system data were sampled.
    [14]
    14. The method of claim 12, further comprising the step of performing a linear regression on the system data and on the refrigerant level data in order to determine an unmatched linear combination of the system data that estimates the level of soda.
    [15]
    15. Method according to claim 14, further comprising the step of determining an error table having corresponding entries
    'teeth to a difference between the estimated refrigerant level and the refrigerant level data at a particular time of day based on a result of linear regression.
    [16]
    16. Method, according to claim 15, further comprising the step of generating a table that stores an hour effect indicating the amount of effect that an hour of a particular day has on the refrigerant level data for each hour of the particular day.
    [17]
    17. The method of claim 16, further comprising the step of carrying out an hourly compensated linear regression effect in order to determine a linear combination of the system and system data. hour effect in order to estimate the level of refrigerant.
    [18]
    18. Method according to claim 11, in which the notification "indicates whether the refrigerant has been added to the refrigeration system when the difference between the expected refrigerant level and the refrigerant level reading is greater than than the upper control limit in a predetermined number of consecutive readings.
    [19]
    19. Method, according to claim 11, in which the generated notification indicates whether the refrigerant is leaking from the system when the difference between the expected refrigerant level and the refrigerant level reading is less than the limit lower control in a predetermined number of consecutive readings.
    [20]
    20. The method of claim 11, wherein the system data includes an ambient temperature reading, a condenser temperature reading and a discharge pressure reading.
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    同族专利:
    公开号 | 公开日
    AU2010319488A1|2012-05-03|
    EP2499435A4|2017-08-16|
    EP2499435B1|2019-01-16|
    CA2777349C|2015-01-06|
    CN102667352B|2014-12-24|
    US20110112814A1|2011-05-12|
    CA2777349A1|2011-05-19|
    EP2499435A2|2012-09-19|
    CN102667352A|2012-09-12|
    AU2010319488B2|2014-02-27|
    WO2011060121A2|2011-05-19|
    MX2012005122A|2013-01-24|
    WO2011060121A3|2011-08-18|
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    法律状态:
    2020-10-06| B06F| Objections, documents and/or translations needed after an examination request according [chapter 6.6 patent gazette]|
    2020-10-20| B06U| Preliminary requirement: requests with searches performed by other patent offices: procedure suspended [chapter 6.21 patent gazette]|
    2021-02-02| B11B| Dismissal acc. art. 36, par 1 of ipl - no reply within 90 days to fullfil the necessary requirements|
    2021-11-23| B350| Update of information on the portal [chapter 15.35 patent gazette]|
    优先权:
    申请号 | 申请日 | 专利标题
    US26022209P| true| 2009-11-11|2009-11-11|
    US61/260,222|2009-11-11|
    US12/943,626|US20110112814A1|2009-11-11|2010-11-10|Refrigerant leak detection system and method|
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    PCT/US2010/056315|WO2011060121A2|2009-11-11|2010-11-11|Refrigerant leak detection system and method|
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