• 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:
    computer-based method for determining residual life expectancy of a gas turbine rotor and device configured to determine residual life expectancy of a gas turbine component the present invention generally relates to methods and systems and, more particularly, mechanisms and techniques for predicting and optimizing the useful life of a gas turbine or its components. the computer-based method for determining the residual life expectancy of a gas turbine rotor comprises receiving operating conditions of the gas turbine on the computer (500); receiving a gas turbine rotor inspection result (502); update, based on the gas turbine operating conditions and the gas turbine rotor inspection result, a database for a gas turbine class fleet that has a set of common features that correspond to the gas turbine (504); and calculate the residual life expectancy of the gas turbine rotor and a risk associated with prolonging its useful life (506).
    公开号:BR112014001652B1
    申请号:R112014001652-6
    申请日:2012-07-25
    公开日:2021-04-13
    发明作者:Roberto De Prosperis；Maciej Borkowski；Paolo DI SISTO
    申请人:Nuovo Pignone S.P.A.；
    IPC主号:
    专利说明:

    FIELD OF THE INVENTION
    [001] The present invention relates, generally, to methods and systems and, more particularly, to mechanisms and techniques for the prediction and optimization of the useful life of a gas turbine or components thereof. BACKGROUND OF THE INVENTION
    [002] Figure 1, which is similar to Figure 1 of the publication of patent application No. US2008 / 0243352 (incorporated by reference in this document), illustrates an example of a gas turbine 10 having a compressor 12, a combustion 14, a turbine 16 coupled to the compressor 12 and a computer control system (controller) 18. An inlet duct 20 for the compressor 12 can supply the compressor 12 with ambient air. The inlet duct 20 may have ducts, filters, screens and noise reduction devices that contribute to a loss of ambient air pressure that flows through the inlet 20 and into the inlet fins 21 of the compressor 12. A duct exhaust 22 for the turbine directs the flue gases from the outlet of the turbine 10 through, for example, emission control and noise reduction devices. The turbine 10 can drive a generator 24 that produces electrical energy. Alternatively, whenever the turbine is a two-axis device (for example, which includes a high pressure turbine and a low pressure turbine), the low pressure turbine, which can rotate at a different speed than the high pressure rotor, it can drive a more generic machine like a compressor or even a generator.
    [003] The operation of the gas turbine 10 can be monitored by several sensors 26 designed to measure different variables related to the performance of the turbine 10, the generator and the environment. For example, groups of redundant temperature sensors 26 can monitor the ambient temperature surrounding the gas turbine 10, compressor discharge temperature, gas exhaust temperature of the turbine and other temperature measurements of the gas stream through the gas turbine. 10. Similarly, groups of redundant pressure sensors 26 can monitor ambient pressure and static and dynamic pressure levels at the turbine exhaust from the compressor inlet and outlet at other locations in the gas stream through the gas turbine 10. The groups of redundant humidity sensors 26, for example, wet and dry bulb thermometers, can measure the ambient humidity in the inlet duct of compressor 12. The redundant sensor groups 26 may also include flow sensors, speed sensors, temperature sensors flame detection, valve position sensors, guide fin angle sensors or the like, which capture various parameters relevant to the operation of the tur gas turbine 10. As used in this document, "parameters" refer to items that can be used to define the operating conditions of the turbine, such as temperatures, pressures and gas flows at defined locations in the turbine, but not limited to those conditions mentioned above.
    [004] Also, the fuel control system 28 regulates the fuel that flows from a fuel supply to the combustor 14, one or more divisions between the fuel that flows into a primary and a secondary fuel nozzle and the amount of fuel mixed with secondary air flowing into a combustion chamber. The fuel control system 28 can also select the type of fuel for the combustion. The fuel control system 28 can be a separate unit or it can be a component of the main controller 18. Controller 18 can be a computer system that has at least one processor that performs programs and operations to control the operation of the gas turbine which uses sensor inputs and instructions from human operators. The commands generated by controller 18 can induce actuators in the gas turbine to, for example, adjust valves (actuator 27) between the fuel supply and combustors that regulate the flow, the fuel divisions and the type of fuel that flow to the combustors ; adjust inlet guide fins 21 (actuator 29) on the compressor; adjust inlet leakage heat; as well as activating other control settings on the gas turbine.
    [005] The turbine can have a wide application in the oil and gas field. That is, it can activate compressors in the pipelines, as well as it can also activate compressors pumping oil or natural gas from wells. An important critical quality (QCI) for oil and gas companies is the availability of their plants to then maximize production. To minimize plant shutdown or interruption, parts of the plant's core, such as the gas turbine, are ideally replaced / maintained only when their probability of failure has a substantial impact on plant safety. Another important QCI is the cost of maintenance, which should be minimized whenever possible.
    [006] To improve these and other QCIs, several entities have launched life expectancy initiatives such as CBM (maintenance-based conditions) and GVD (rotor life management). Some techniques rely only on optical inspections for the measurement of slit lengths to develop a statistical distribution of slit lengths. This statistical distribution is used to estimate the life expectancy of the equipment. Another technique involves inspecting a component for damage or deterioration (for example, micro cracks), forming a structural device model, configuring a future condition of use for the device and simulating a damage or deterioration advance. Another approach is to estimate propagation damage using an expression that includes a Larson-Miller expression and then perform statistical analysis (for example, Weibull statistical analysis) to estimate future propagation damage. Here, an estimation parameter based on an initial equipment count and thermal stress is calculated based on a statistical model. Another approach is to determine a relationship between a metal temperature of a turbine component and an operating condition of the turbine that houses the component. This approach uses a thermal model of the component and a history of turbine operations to predict a current or future operating temperature of the component.
    [007] However, conventional methods and systems for extending the life of the plant and turbine require the plant to be closed for inspections to collect data (for example, gap lengths) and, in general, are not applicable to components other than show obvious flaw. Consequently, it will be desirable to provide systems and methods that avoid the problems and disadvantages described above. DESCRIPTION OF THE INVENTION
    [008] Aspects of the present invention relate to systems and methods for predicting and optimizing the service life of gas turbine components (for example, rotors), especially components that do not show evident damage and for which it is expected a long service life, but which, due to high thermomechanical loads, can crack and degenerate quickly in an engine failure that can compromise the plant's safety.
    [009] According to an exemplary embodiment, there is a computer-based method for determining the residual life expectancy of a gas turbine rotor. The method includes receiving gas turbine operating conditions on the computer; receive a gas turbine rotor inspection result; update, based on the gas turbine operating conditions and the gas turbine rotor inspection result, a database for a fleet corresponding to the gas turbine; and calculate the residual life expectancy of the gas turbine rotor. BRIEF DESCRIPTION OF THE DRAWINGS
    [010] The attached drawings, which are incorporated into the specification and form part of it, illustrate one or more achievements and, together with the description, explain those achievements. In the drawings: Figure 1 shows an example of a gas turbine; Figure 2 illustrates four concepts that serve as the basis for an embodiment of the invention; Figure 3 illustrates analytical phases associated with an embodiment of the invention; Figure 4 illustrates a method for predicting component life according to an embodiment of the invention; Figure 5 illustrates another method for predicting the service life of a gas turbine component in accordance with an exemplary embodiment; and Figure 6 illustrates a device configured to implement the method discussed in this document. DESCRIPTION OF ACCOMPLISHMENTS OF THE INVENTION
    [011] The following description of the exemplary achievements refers to the attached drawings. The same reference numbers in different drawings identify the same or similar elements. The following detailed description does not limit the invention. Instead, the scope of the invention is defined by the appended claims. The following achievements are discussed, for the sake of simplicity, in relation to the terminology and structure of a gas turbine, particularly a rotor. However, the achievements that will be discussed later are not limited to these systems, but can be applied to other systems.
    [012] The reference throughout the specification to "one (1) realization" or "an realization" means that a specific feature, structure or characteristic described in relation to an realization is included in at least one realization of the disclosed matter. Thus, the appearance of the phrases “in one (1) realization” or “in one realization” in various places throughout the specification does not necessarily refer to the same realization. In addition, particular resources, structures or characteristics can be combined in any suitable way into one or more achievements.
    [013] Gas turbines require periodic inspections to ensure the minimization of operational and safety risks. Maintenance factors for rotors include start and operating hours. These inspections assess the health of a customer's rotor through the compilation and use of pertinent information, which include: operational variables (model time, cycle and temperature to assess its impacts on propagation and low cycle fatigue); material history (which includes models of the application environment, pipeline cycle and maintenance practices); subcomponent condition (determined by rigorous investigation of damage and adjustment subcomponents). With this information, it is possible to make an appropriate recommendation for removing or extending the rotor life. When the replacement is authorized, the innovative process can provide recommendations regarding the possibility of a kind replacement or the installation of a new rotor.
    [014] As shown in Figure 2, according to one embodiment, there is a method for predicting component life with greater accuracy than is possible with conventional systems and methods. This method can be implemented in a forecasting device 20 which will be discussed in more detail later. The method is based on four concepts: physics-based models for predicting a probabilistic useful life of one or more gas turbine components (21), results of a complete inspection of the components (22), results of a history assessment the service life of the engine to which the part (23) belongs and results of destructive tests in some parts (24). These four concepts can be linked to a method that has multiple phases as discussed below.
    [015] In one embodiment, there are 6 phases as shown in Figure 3. Although the 6 phases are described, a person skilled in the art would know that some phases can be skipped and others can be added. Also, although Figure 3 shows phases that flow consecutively from 1 to 6, feedback between the phases is possible, as discussed below.
    [016] In Figure 3, numerical / analytical models of phase 1 (31) feed several transfer functions of phase 2 (32). The results of the transfer functions feed into various probabilistic analyzes (33) of phase 3. The results of probabilistic analyzes can be used with destructive test results from phase 4 (34) to predict the useful life of a given component. In addition, the non-destructive inspection and operation history of the components shown in step 5 (35) can also be used during the prediction of the useful life of a given component. The results of each of the first 5 phases can be combined in a phase 6 life extension forecast (36). The details of each of these 6 phases are now described below. Also, although destructive tests (34) are useful for the process, these destructive tests are usually carried out only once, to then characterize the material used in the rotor after many hours of useful life. In contrast, non-destructive tests (inspections) can be performed every time maintenance is performed on a rotor for which the expected life span is expected.
    [017] Step 1 of an embodiment of the invention includes the creation of numerical / analytical models that have the ability to predict, for example, a) flow, pressure and temperature of primary and secondary engine flows; b) heat transfer coefficients between flows and rotors; c) rotor metal temperatures; and d) displacements, stretches and rotor tension. In one embodiment, the models are aimed at an “ideal” gas turbine or components, that is, based exclusively on design and manufacturing specifications that are unchanged by “real-world” experiences. In another embodiment, one or more of the models may include “real-world” parameters. One or more of these models can be specific to a type of gas turbine or can be generic. Models, as well as forecasting tools, may be commercially available or may be developed in-house.
    [018] Numerical / analytical models are designed to be operated in series or in parallel, depending on a total scenario or device to be modeled. In one embodiment, the numerical / analytical models will use one or more independent input variables (vitalX’s) and will emit dependent variables (for example, metal temperatures). The created models can be stored in a model library or toolkit for later use, for example, a memory. These models use dependent variables as input and have the ability to calculate several dependent variables that characterize the gas turbine. Examples of independent variables, vitalXs, are an ambient temperature, a rotor speed, a firing temperature, various internal geometric clearances, etc. associated with a turbine.
    [019] Phase 2 of an embodiment of the invention includes: a) defining a specific and appropriate design of experiment plan (EDE); b) apply one or more of the models developed in phase 1 in the EDE, thereby generating corresponding dependent variables; and c) create transfer functions (FTs) between c1) service life variables [Z] (for example, initiation of cycle crack for FBC (low cycle fatigue), hours for initiation of crack propagation, hours / cycles for crack propagates in a fault, etc.) and c2) the independent variables (vitalX's) of the corresponding models. Transfer functions are those known in the art or new functions defined for this process or a combination of both. A service life variable is generally a variable that represents a concept of maintenance or interest in the service life of the gas turbine or turbine component. With respect to the slot initiation FTs (FBC), preferably, there is at least one FT for each single part to then allow a replacement of only the single part that is at the end of its life or a replacement of the complete rotor, depending on the which is more convenient for the customer. Preferably, there will be at least one crack propagation FT for each surface, so that it has the capacity to obtain one or more indications and to reduce the amount of scrap.
    [020] Phase 3 of an embodiment of the invention includes: a) estimation / definition of distributions of vitalX’s; b) execution of one or more MonteCarlo simulations to determine the probabilistic useful life of a fleet and / or a failure fleet risk; and c) execution of one or more MonteCarlo simulations to configure, for example, a wheel space alarm (confirmation or update of existing configuration, whenever evaluating mature fleet) to minimize the risk of failure. A fleet is understood here to describe a class of gas turbines that have the same group of characteristics in common. The wheel space alarm is a possible example of possible alarms in a gas turbine and can be decreased or increased based on the calculations described in this document.
    [021] A distribution of a vitalX is now described. It is assumed that several gas turbines in a fleet are supplied to several customers, located around the world. An ambient temperature of the gas turbine is an independent variable, that is, vitalX. However, an ambient temperature in Doha (Qatar) is different from an ambient temperature in Alaska. Thus, for a given vitalX (Tamb), there is a temperature distribution for the gas turbines that make up the fleet. This distribution can be, for example, a Gauss bell or take other forms.
    [022] If the number of data points for any measured vitalX is large enough, it will be possible to develop a reasonably accurate destruction function for vitalX. Then, filters (for example, Kalman filters) can be applied to coordinate the models with field data (for example, slit length). However, if the number of data points for each vitalX measured is not large enough, these filters may not provide accurate results. In that case, an engineering judgment can be used to develop a “best guess” distribution function for vitalX. In this case, the distribution is defined.
    [023] Returning to the wheel space alarm, it is noted that combustion turbines include a compressor that has a plurality of stages that create a flow of compressed air and a turbine that has a turbine rotor that drives an axle. During operation, temperatures in the turbine rotor rise significantly. Cooling can be provided by directing compressor discharge air directly into a wheel space that extends around the turbine rotor. The temperature of the wheel space can be maintained at a material limit between the compressor discharge temperature and the hot gas path temperature. In the event that the wheel space temperature exceeds the material limit, an alarm can be sounded to indicate an overtemperature condition. When the material limit is exceeded, the turbomachine can be closed and, after accurate inspection and study, one or more devices can be applied to provide additional cooling flow. Several factors can affect the compressor's exhaust air temperature. For example, as the ambient inlet air temperature rises, the discharge air from the compressor rises. Thus, the correct configuration of a wheel space alarm is desirable and the innovative process described in this document has the ability to adjust that alarm.
    [024] The preceding phases (31 to 33) are applicable to the prediction of a total life span of a device / component and to the prediction of a residual life span (ie optimization of remaining life). The following steps are generally more applicable to predicting a residual useful life.
    [025] Phase 4 (34) of an embodiment of the invention includes destructive laboratory tests of a statistical amount of scraped parts (in the middle or at the end of their useful lives) to confirm that the strength of the material is still consistent with expectations from the project.
    [026] Phase 5 (35) of an embodiment of the invention includes the acquisition and operation of a database of historical operating conditions for each specific unit. The data in the database may include a) the results of any non-destructive tests and b) inspections during the life of the component. Inspections may include one or more of the following types: eddy current inspections, magnetic particle inspections, penetrating fluorescent inspections and / or ultrasonic inspections. Optimistically, such tests and inspections will be carried out on 100% of the parts' surfaces. The interest is the identification of operating histories that may indicate that a unit may have been operated under more favorable conditions than those that were assumed when a manufacturer's expected life expectancy was estimated.
    [027] Phase 6 of an embodiment of the invention includes using the results of the previous 5 phases to estimate an extension of the useful life of a specific part or device (ie estimating the risk of failure associated with extending a service life of component beyond an estimated end-of-life of initial manufacture). This estimation may employ statistics that involve some or all vitalXs that affect the life expectancy of the part (for example, operating conditions, geometric dimensions, material properties). To simplify calculations, the number of vitalXs in this phase (as well as in phase 3) can be reduced (for example, to less than 7 vitalXs). This reduction in vitalXs can be facilitated by the creation (in phase 2) of linear FTs associated with different groups of vitalXs. These linear FTs can be used to select, from each group of vitalXs, the vitalX that most affects the corresponding life variable.
    [028] An objective of phase 6 is to use data from the previous 5 phases to predict whether a component may have a longer service life than that predicted by the original designer (who may have used more useful life calculations for conservative products, or who may have assumed a more severe operating environment than the current operating environment identified in step 5, or who may have assumed a material strength greater than the strength of the actual material identified in Step 4).
    [029] The preceding comments provided an overview of at least 6 phases that can be combined to generate a more accurate estimate of product life. The following comments provide additional details on at least some of these 6 phases.
    [030] In one embodiment, phase 4 inspections can be used to support (but not validate) phase 6 calculations, as well as to confirm that a part has been manufactured according to the design and that that part has been operated and maintained according to the project. Phase 4 inspections are preferably not used to validate phase 6 calculations due to the fact that non-destructive phase 4 inspections are often unable to detect local fatigue or propagation damage and therefore are often unreliable to assign a value to the accumulated damage.
    [031] Preferably, the phase 6 life extension forecasts should rely on a phase 4 material database that is consistent with the estimated service life. Preferably, a phase 4 material propagation database should include tests of not less than 1/20 of the expected useful life. In other words, the database must include enough information (in terms of time) to be reliable. An example in this sense is like the following. A gas turbine rotor is assumed to have an expected service life of 20 years. It is desirable that the database includes at least the importance of 1 year of information about the rotor, that is, not less than 1/20 of the expected service life.
    [032] The results of phase 3 can be used to predict a probabilistic fleet life that is different from a product specification. Before and after using phase 3 results, any “unexpected” results should be investigated to determine whether any vitalX models and / or distributions should be modified.
    [033] The results of phase 5 can highlight unexpected defects in one part (for example, cracks). If unexpected defects are covered up in phase 5, a root cause analysis (ACR) must be performed. ACR results can be applied to phase 6 to further coordinate the life extension estimate, and can be applied to design and manufacturing to optimize the design and production of the initial component. In phase 5, the cracks can be considered as an indication of an end of life of the part and must be investigated through ACR to then check if forecasting models and FTs can still be considered reliable or if they require modification. . Alternatively, non-crack indications (for example, scratches, marks, impressions) may be acceptable, but only if those non-crack indications do not appear to affect the part's useful life. Defect re-machining can then be suggested to make indications smoother.
    [034] These non-crack indications (usage indicators) can be assessed through the generation of dedicated FTs. (The FT will have the same vitalXs as the other FT plus the dimensions of the indication (length, depth, thickness). As noted above, in order to reduce the amount of FTs, an embodiment can group part surfaces and define only one FT for each group of surfaces. Each group can be composed according to surfaces that have a similar behavior, so that a final group that has a worse location can be selected as being representative of the total zone. A rotor can have hundreds if not thousands of small surfaces.
    [035] The previously described phases can be combined with the process flow of Figure 4. The process can start with a turbine rotor inspection (41). Inspection determines whether cracks are present (42) or whether other indications related to the life cycle (for example, scratches, marks, impressions) are present (43). If cracks, due to operations, are present, a root cause analysis (ACR) is performed (48). ACR results can be fed back to any forecasting models used by the manufacturer, as well as any life span analysis. If other indications related to the life cycle (for example, scratches, marks, impressions) are discovered, these indications can be combined with results from operating history (44) to estimate a residual useful life for each indication. If neither cracks nor other indications are detected, fleet distributions can be updated to predict the residual useful life of a specific unit (46). For example, Y distributions, for an entire fleet, are generated using vitalX distributions that include the total variability present in the fleet (for example, the distribution will also include all possible locations where machines can operate). Also, the specific unit Y, which has distributions that are included in the fleet distributions, typically maintains a common shape (for example, a Gaussian bell curve). However, these distributions can be narrow due to the fact that one or more vitalX distributions can be replaced with a value (for example, internal clearance) or replaced with more narrow distributions (for example, local ambient temperature). This calculation of residual useful life can also take into account results from operating history (44). The updated fleet distribution can be used to predict total risk for a unit (47). Examples of predicted risks include predictions for crack initiation and / or the propagation of an existing crack.
    [036] In the process of Figure 4, items 41, 42, 43 and 44 can be considered as part of phase 5, with the remaining items (45, 46, 47 and 48) being associated with phase 6. One or more from phases 1 to 4 previously described can feed results to phase 4. This is useful because most turbine FEOs design their components, so that they do not form cracks during their useful life.
    [037] The methodology described above is aimed at gas turbines currently installed and in operation. However, a person skilled in the art would know that at least the first three are applicable to a new project (for example, life predictions for new projects).
    [038] The preceding methodology can be performed on one or more processor-based devices (computers) that may or may not be connected via a wired or wireless communications network. The processor-based device includes a processor, memory and an input unit. The processor-based device may also include a monitor. Such a device is discussed with reference to Figure 6. The computer programs associated with the preceding methodology can be stored in a non-transitory storage medium, such as a memory, disk or other device.
    [039] According to an exemplary embodiment illustrated in Figure 5, there is a method for determining a residual life expectancy for a gas turbine rotor. The method includes a step 500 of receiving gas turbine operating conditions on the computer; a step 502 of receiving a gas turbine rotor inspection result; an upgrade step 504, based on the gas turbine operating conditions and the gas turbine rotor inspection result, a database for a fleet that corresponds to the gas turbine; and a step 506 for calculating the residual useful life expectancy of the gas turbine rotor.
    [040] The above method can be deployed in a device to determine a residual useful life expectancy for a gas turbine component. The device can include a memory and a processor operationally connected to the memory. The processor can be configured to receive the gas turbine operating conditions, receive a gas turbine rotor inspection result, update, based on the gas turbine operating conditions and the turbine rotor inspection result a gas, a database for a fleet that corresponds to the gas turbine, and calculate the residual life expectancy of the gas turbine rotor.
    [041] The processor can be additionally configured to use physical models of the gas turbine to calculate numerous gas turbine dependent variables based on independent variables or to provide the independent variables as inputs for the transfer of functions to calculate service life variables or to receive distributions of the independent variables or to run MonteCarlo simulations based on the independent variables, on the distributions of the independent variables and on the transfer functions to determine the probabilistic life of the fleet, or to receive destructive laboratory tests performed on parts of the turbine gas, or to calculate the residual life expectancy of the gas turbine rotor based on independent variables, transfer functions, MonteCarlo simulations and destructive laboratory tests.
    [042] In an exemplary embodiment, a non-transitory computer-based product contains instructions for determining the residual life expectancy of a gas turbine component, the computer-based product is organized to induce a processor-based device to carry out the following instructions: receive operating conditions of the gas turbine; receive a gas turbine rotor inspection result; update, based on the gas turbine operating conditions and the gas turbine rotor inspection result, a database for a fleet that corresponds to the gas turbine; and calculate the expected residual service life of the gas turbine rotor and a risk associated with the service life extension.
    [043] An example of a representative device, capable of performing operations according to the achievements discussed above, is illustrated in Figure 6. The hardware, firmware, software or a combination of them can be used to perform various steps and operations described in this document. The computing structure 600 of Figure 6 is an exemplary computing structure that can be used in connection with such a system.
    [044] The exemplary computing arrangement 600 suitable for carrying out the activities described in the exemplary embodiments may include a server 601. Such a server 801 may include a central processor (CPU) 602 coupled with a random access memory (RAM) 604 and a read-only memory (ROM) 606. ROM 606 can also be other types of storage media for storing programs, such as programmable ROM (PROM), erasable PROM (EPROM), etc. The processor 602 can communicate with other internal and external components through a set of input / output (I / O) circuits 608 and bus 610, to provide control signals and the like. The 602 processor performs a variety of functions as is known in the art, as dictated by software and / or firmware instructions.
    [045] Server 601 may also include one or more data storage devices, which include 612 hard and flexible disk drives, 614 CD-ROM drives, and other hardware capable of reading and / or storing information such as DVD, etc. . In one embodiment, the software, to perform the steps discussed above, can be stored and distributed on a 616 CD-ROM, 618 diskette or other media format capable of storing information in a portable manner. This storage media can be inserted and read through devices, such as CD-ROM drive 614, disk drive 612, etc. The 601 server can be attached to a monitor 620, which can be any type of known monitor or presentation screen, such as LCD screens, plasma screens, cathode ray tubes (CRT), etc. A 622 user input interface is provided, which includes one or more user interface mechanisms such as a mouse, keyboard, microphone, touch pad, touch screen, voice recognition system, etc.
    [046] The 601 server can be coupled to other computing devices, such as fixed and / or wireless telephone terminals, through a communications network. The server can be part of a larger communications network configuration such as a global area network (GAN) such as the internet 628, which allows for the final connection to the various fixed and / or mobile phone client / observer devices.
    [047] The exemplary embodiments revealed provide a method, computer software and device for determining the remaining service life of a gas turbine component. It should be understood that this description is not intended to limit the invention. On the contrary, the exemplary embodiments are intended to encompass the alternatives, modifications and equivalents, which are included in the spirit and scope of the invention as defined by the appended claims. In addition, in the detailed description of the exemplary embodiments, several specific details are presented in order to provide a comprehensive understanding of the claimed invention. However, a person skilled in the art would understand that various achievements can be practiced without such specific details.
    [048] Although the resources and elements of the exemplary achievements present are described in the achievements in particular combinations, each resource or element can be used alone without the other resources and elements of the achievements or in various combinations with or without other resources and elements revealed in them . The methods or flowcharts provided in the present patent application may be implemented in a computer program, software or firmware tangibly incorporated in a computer-readable storage medium for execution by a specifically programmed processor or computer.
    [049] This written description uses examples from the revealed material to allow the technician in the subject to practice the same, including producing and using any devices or systems and performing any built-in methods. The patentable scope of the matter is defined by the claims and may include other examples that occur to those skilled in the art. Such other examples are intended to be within the scope of the claims.
    权利要求:
    Claims (10)
    [0001]
    1. COMPUTER-BASED METHOD FOR DETERMINING A RESIDUAL LIFE EXPECTATION OF A GAS TURBINE ROTOR, which comprises: operating the gas turbine, obtaining and receiving operating conditions of the gas turbine on the computer (500); inspect the gas turbine rotor, obtain and receive a gas turbine rotor inspection result (502); the method characterized by updating, based on the gas turbine operating conditions and the gas turbine rotor inspection result, a database for a gas turbine class fleet that has a set of common features that corresponds the gas turbine (504); and calculate the residual life expectancy of the gas turbine rotor and a risk associated with prolonging the service life (506), in which the calculation step also comprises: measuring independent variables of the gas turbine; and using physical models of the gas turbine to calculate a plurality of gas turbine dependent variables based on independent variables.
    [0002]
    2. METHOD, according to claim 1, characterized by the gas turbine rotor inspection result indicating the presence of a crack in the rotor or an indication of use in the rotor or that there is no event in the rotor.
    [0003]
    METHOD, according to any one of claims 1 to 2, characterized in that, in the event of an unexpected crack in the rotor, it comprises modifying a gas turbine model.
    [0004]
    4. METHOD according to any one of claims 1 to 3, characterized in that, if there is an indication of use in the rotor or an expected crack, it comprises calculating a residual life of the rotor for the indication of use or an expected crack.
    [0005]
    5. METHOD, according to any one of claims 1 to 4, characterized by the indication of use being among a mark, scratch, or impression on the rotor.
    [0006]
    6. METHOD, according to any one of claims 1 to 5, characterized in that it further comprises: providing independent variables as inputs to transfer functions in order to calculate life variables, in which a life variable is a parameter of cycle crack initiation for LCF (low cycle fatigue).
    [0007]
    7. METHOD, according to any one of claims 1 to 6, characterized by also comprising: running MonteCarlo simulations based on the independent variables and on the distributions of the independent variables.
    [0008]
    8. METHOD, according to any one of claims 1 to 6, characterized by also comprising: running MonteCarlo simulations based on independent variables, independent variable distributions and transfer functions to determine the probabilistic life of the fleet.
    [0009]
    9. METHOD, according to any one of claims 1 to 8, characterized by also comprising: calculating the residual life expectancy of the gas turbine rotor based on the independent variables, transfer functions, MonteCarlo simulations and the destructive laboratory tests.
    [0010]
    10. METHOD, according to any one of claims 1 to 9, characterized: by the independent variables being selected from a group formed by: room temperature, rotor rotation speed, firing temperature and geometric sheets or combination thereof; and the dependent variables are selected from a group formed by: cycle crack initiation parameter for LCF (low cycle fatigue), hours for crack propagation initiation, or hours or cycles for a crack to propagate in a fault, or combination thereof.
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    同族专利:
    公开号 | 公开日
    KR101765994B1|2017-08-07|
    RU2014101246A|2015-09-10|
    EP2737376A1|2014-06-04|
    AU2012288840A1|2014-02-13|
    CN103718123A|2014-04-09|
    RU2617720C2|2017-04-26|
    CA2843218C|2019-08-20|
    JP2014522943A|2014-09-08|
    JP6010618B2|2016-10-19|
    AU2016277688A1|2017-01-12|
    WO2013014202A1|2013-01-31|
    CN103718123B|2016-10-26|
    ITCO20110032A1|2013-01-29|
    US20140244133A1|2014-08-28|
    BR112014001652A2|2017-02-21|
    CA2843218A1|2013-01-31|
    KR20140048949A|2014-04-24|
    US9507342B2|2016-11-29|
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    法律状态:
    2018-12-11| B06F| Objections, documents and/or translations needed after an examination request according [chapter 6.6 patent gazette]|
    2019-10-29| B06U| Preliminary requirement: requests with searches performed by other patent offices: procedure suspended [chapter 6.21 patent gazette]|
    2020-10-20| B06A| Notification to applicant to reply to the report for non-patentability or inadequacy of the application [chapter 6.1 patent gazette]|
    2021-02-02| B09A| Decision: intention to grant|
    2021-04-13| B16A| Patent or certificate of addition of invention granted|Free format text: PRAZO DE VALIDADE: 20 (VINTE) ANOS CONTADOS A PARTIR DE 25/07/2012, OBSERVADAS AS CONDICOES LEGAIS. |
    优先权:
    申请号 | 申请日 | 专利标题
    IT000032A|ITCO20110032A1|2011-07-28|2011-07-28|DEVICE AND METHOD OF OPTIMIZATION AND DETERMINATION OF THE LIFE OF A GAS TURBINE|
    ITCO2011A000032|2011-07-28|
    PCT/EP2012/064621|WO2013014202A1|2011-07-28|2012-07-25|Gas turbine life prediction and optimization device and method|
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