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    • 专利摘要:
    A system (20) for monitoring a gas turbine includes a database (26) containing historical parameter information from comparable gas turbines and an input device (34) comprising a device, in particular a computer, adapted to generate a plant data signal (30 ) And a risk signal (32). A processor (22) in communication with the memory and the input device (34) records the attachment data signal (30) into the database (26), calculates information for the gas turbine, and calculates a conditional risk of the gas turbine Will reach a limit. An output signal (42) contains repair or maintenance schedules. A method for monitoring a gas turbine includes receiving information from comparable gas turbines, Add information from the gas turbine to the information from comparable gas turbines and projecting information for the gas turbine into the future. The method further comprises calculating a conditional risk that the gas turbine will reach a limit, and generating an output signal (42) containing repair or maintenance schedules.
    公开号:CH702625B1
    申请号:CH00224/11
    申请日:2011-02-07
    公开日:2017-03-31
    发明作者:Jiang Xiaomo；Edward Bernard Michael；Vittal Sameer
    申请人:Gen Electric；
    IPC主号:
    专利说明:

    Description of the Invention [0001] The present invention broadly comprises a system and method for monitoring the functional state of a gas turbine. In particular, the present invention describes a system and method that adapts a generic gas turbine model using actual information about a single gas turbine to project and / or predict future repair and / or maintenance intervals of the individual gas turbine.
    BACKGROUND OF THE INVENTION [0002] Gas turbines are widely used in industrial and commercial applications. As illustrated in FIG. 1, a typical gas turbine 10 includes an axial compressor 12 at the front, one or more combustion chambers 14 approximately at the center, and a turbine 16 at the rear. Compressor 12 includes a plurality of stages of rotating vanes and stationary vanes. Ambient air enters the compressor 12 and the rotating vanes and stationary vanes increasingly impart kinetic energy to the working fluid (air) to bring it into an energetic state. The working fluid exits the compressor 12 and flows to the combustion chambers 14 where it is mixed with a fuel 18 and ignited to produce combustion gases which have a high temperature, A high pressure and a high speed. The combustion gases leave the combustion chambers 14 and flow to the turbine 16 where they expand to perform work.
    [0003] Gas turbines, like any other mechanical device, require periodic repairs and maintenance to ensure proper functioning. As a general approach, previous experience with the "fleet" of gas turbines, in particular comparable gas turbines of a similar class or design, can be statistically analyzed to develop a fleet model that will predict the expected wear and the expected damage experienced by other gas turbines in the future Can project. Based on the fleet model, scheduling, forecasting, repairs and maintenance can be scheduled at optimum intervals that minimize the risk of unscheduled shutdowns to perform repairs as well as unnecessary shutdowns to prevent unnecessary preventive maintenance.
    [0004] However, the actual behavior of individual gas turbines may differ from the fleet model. For example, individual gas turbines may have minor differences in configuration, manufacturing tolerances, and construction, which may result in other degrees of wear and damage compared to the fleet model. In addition, the operating, repair and maintenance histories actually exhibiting individual gas turbines may differ from the flotation average. For example, gas turbines operating in humid and corrosive environments may require more frequent repair and maintenance operations than the fleet model to cope with corrosion, pitting and emissions problems. Conversely, other gas turbines, which experience fewer start and stop cycles, Compared to the fleet model, require less frequent shutdowns for carrying out preventive maintenance measures in connection with cyclic loads. In each example, adjustments to the fleet model would improve the ability to optimally plan repairs and maintenance on the basis of the actual data associated with individual gas turbines.
    [0005] The object of the present invention is to provide an improved system and method for monitoring the behavior and the functioning of a gas turbine.
    Brief Description of the Invention [0006] Aspects and advantages of the invention are set forth in the following description, or may be apparent from the description, or may be learned by practice of the invention in practice.
    [0007] The present invention relates to a system for monitoring the behavior of a gas turbine in use. The system includes a first storage element containing a database of historical parameter information from comparable gas turbines, and an input device, the input device comprising a device, in particular a computer, configured to generate a plant data signal containing parameter information from the gas turbine in use , And a risk signal is generated which contains a risk benefit for the gas turbine in use, for example, application stress data, permissible risk levels for each fault mechanism and / or the next maintenance interval for the gas turbine in use. A processor, which is in communication with the first storage element and the input device, Takes the installation data signal into the database with parameter information from comparable gas turbines, calculates or predicts parameter information for the gas turbine in use, and calculates a conditional risk that the parameterized parameter information for the gas turbine in use reaches a predetermined parameter limit. An output signal generated by the processor contains at least either repair and / or maintenance planning information.
    [0008] The present invention also relates to a method for monitoring the behavior of a gas turbine in use. The method includes: receiving parameter information from comparable gas turbines, adding parameter information from the gas turbine in use to the parameter information of comparable gas turbines, and prognosticizing or parameterizing parameter information for the gas turbine in use. The method further includes calculating a conditional risk that the parameterized parameter information for the gas turbine in use will reach a predetermined parameter limit and generating an output signal including at least one of a repair and maintenance plan for the turbine in use, On the basis of the conditional risk.
    Furthermore, a method for monitoring the behavior of a gas turbine in use is disclosed which includes receiving a fleet model signal containing parameter information from comparable gas turbines, adding parameter information from the gas turbine in use to the parameter information of comparable gas turbines, and projected or projected Of parameter information for the gas turbine. The method further comprises calculating a conditional risk that the predicted parameter information for the gas turbine in use will reach a predetermined parameter limit and generating an output signal including at least one of a repair plan and a maintenance plan and a
    [0010] Those skilled in the art will better understand the features and aspects of such and further embodiments after reviewing the description.
    BRIEF DESCRIPTION OF THE DRAWINGS [0011] A complete and implementation-enabling disclosure of the present invention, including its best mode, will be disclosed to those skilled in the art in greater detail in the remaining description which includes a reference to the accompanying figures in which:
    FIG. 1 illustrates a simplified block diagram of a typical gas turbine system; FIG.
    FIG. 2 illustrates a functional block diagram of a system for monitoring a gas turbine in use according to an embodiment of the present invention; FIG.
    FIG. 3 illustrates an algorithm for updating and validating a fleet model; FIG.
    FIG. 4 illustrates an algorithm for updating and validating a plant model; FIG.
    FIG. 5 illustrates an algorithm for performing a risk analysis analysis; FIG.
    FIG. 6 illustrates an algorithm for calculating the remaining useful life for a part or a component; FIG.
    FIG. 7 graphically illustrates hypothetical damage curves which can be generated by a risk analysis according to an embodiment of the present invention; FIG. and
    FIG. 8 graphically illustrates hypothetical useful life curves according to an embodiment of the present invention.
    DETAILED DESCRIPTION OF THE INVENTION [0012] Reference will now be made in detail to the present embodiments of the invention, one or more examples of which are illustrated in the accompanying drawings. The detailed description uses terms with numbers and letters to refer to features in the drawings. Identical or similar reference numerals in the drawings and the description are used to refer to like or similar parts of the invention.
    [0013] Each example is intended to illustrate the invention, not to limit the invention. Indeed, it will be apparent to those skilled in the art that modifications and changes can be made to the present invention without departing from the scope or scope thereof. For example, features illustrated or described as part of an embodiment may be used in another embodiment to provide yet another embodiment. Thus, it is intended that the present invention include such modifications and alterations as fall within the scope of the appended claims and their equivalents.
    [0014] The systems and methods described herein relate to processors, servers, storage, databases, software applications, and / or other computer-based systems, as well as to measures taken or taken by such systems, and information directed to and from such systems Systems. One skilled in the art will recognize that the inherent flexibility of computer-based systems allows for a wide variety of possible configurations, combinations, and division of tasks and functionalities to and among the components. For example, computers described herein may implement implemented processes using a single server or processor or multiple such elements that operate in combination with each other. Databases and other storage media elements and applications can be implemented on a single system or distributed across multiple systems. Distributed components can operate sequentially or parallel to each other. All such variants are intended to fall within the scope and scope of the present subject, as will be understood by those skilled in the art.
    When data is obtained between a first computer system and a second computer system, a first and a second processing device or a component thereof, or the data are accessed, the actual data can be exchanged directly or indirectly between the systems. For example, if a first computer accesses a file or data from a second computer, the access may comprise one or more intermediary computers, proxies, or the like. The actual file or data may be passed between the computers, or a computer may provide a pointer (pointer) or metafile that the second computer uses to access the actual data from a computer other than the first computer.
    [0016] The various computer systems described herein are not limited to any particular hardware architecture or configuration. Embodiments of the methods and systems as set forth herein may be implemented by one or more general-purpose or customer-specific computing devices that are arranged in any suitable manner to provide the desired functionality. The device (s) may be configured to provide additional functionality that is either complementary to or in no way related to the subject matter. For example, one or more arithmetic means may be arranged to access, through access to software instructions written in a computer-readable form, The described functionality. If software is used, any suitable programming language, script language, or other appropriate language, or combinations of languages, may be used to implement the teachings contained herein. However, software must not be used exclusively or at all. As will be understood by those skilled in the art without requiring further detailed explanation, some embodiments of the methods and systems disclosed and disclosed herein may also be implemented using wired logic or other circuitry, including, but not limited to, application specific circuits.
    [0017] It will be understood by those skilled in the art that embodiments of the methods disclosed herein may be embodied by one or more suitable computing devices that make the device (s) operable to perform such methods. As mentioned above, such devices can access one or more computer-readable media containing computer-readable instructions which, when executed by at least one computer, cause the at least one computer to perform one or more embodiments of the methods according to the subject matter , Any suitable computer-readable medium or media may be used to implement or perform the presently disclosed subject, including, but not limited to,
    State-dependent maintenance systems apply stochastic analyzes of fleet models, plant-specific data, and operator-selected risk parameters to create a cost-effective system and method for optimizing repair and / or maintenance intervals of high-quality systems, such as gas turbines. A fleet model can be developed for any specific fault or damage mechanism for a gas turbine by applying multi-stage stochastic modeling techniques, such as Bayesian disorder and market chain-to-space (MCMC) simulation, to historical flotation data. The accuracy of each fleet model can be periodically verified and / or validated, and plant-specific data obtained from a particular gas turbine, Can be added to each fleet model to adapt or update the fleet model or to create a plant model that more precisely models the particular gas turbine for each specific fault mechanism. Applying operator-selected risk parameters to the updated fleet model improves the ability to schedule repair and / or maintenance units at optimum intervals that increase deployment availability, reduce unscheduled and unnecessary shutdowns, and / or increase the service life of the particular gas turbine.
    [0019] If, as an example, the plant-specific data for the particular gas turbine indicate lower wear or minor damage compared to the up-grades provided by the fleet model (forecasts), the availability of the particular gas turbine can be improved by reducing the intervals between repair- And / or maintenance events. Conversely, if the plant-specific data for the particular gas turbine indicate greater wear or damage compared to the forecasts provided by the fleet model, the intervals between repair and / or maintenance applications may be reduced, resulting in a systemic standstill instead of the more expensive unscheduled standstill ,
    [0020] FIG. 2 shows a system 20 for monitoring a gas turbine in the insert 10 according to an embodiment of the present invention. The term "gas turbine in operation" means a specific or special gas turbine, in contrast to the fleet of gas turbines. The system 20 generally includes a processor 22 that includes programming to access one or more memory / media elements. The processor 22 receives a fleet model signal 24 from a database 26 and a flotation data signal 28, a plant data signal 30 and / or a risk signal 32 from an input device 34. The term "signal" refers to any electrical transmission of information or data. The fleet model signal 24 has parameter information for comparable gas turbines, Which are calculated by a fleet model contained in the database 26. The system 20 applies multi-stage stochastic modeling techniques, Bayesian interference, and MCMC simulation, as illustrated by the block 36 and the algorithm illustrated in FIG. 3, to verify and validate the high-parameter parameter information contained in the fleet model signal 24 and An updated fleet model signal 33. The system 20 adds parameter information from the gas turbine in the insert 10 contained in the plant data signal 30 to the updated fleet model signal 33 to produce a fleet model, which is preferably referred to as a plant model and is represented by the block 38 and the fleet model shown in FIG 4 is illustrated.
    [0021] The processor 22 discussed herein is not limited to any particular hardware architecture or configuration. Rather, the processor 22 may include a general-purpose or customer-specific computing device that is configured to access databases and other hardware in a manner described by software instructions described in FIG A computer-readable form, or by a programmed circuit to provide the described functionality. For example, the processor 22 may include a single server, a single microprocessor, wired logic, including, but not limited to, application-specific circuits, or multiple such elements operating in combination with each other.
    The database 26 contains historical parameter information about the "fleet" of gas turbines, in particular comparable gas turbines of a similar class or design, which are collected from available sources. The database 26 may contain storage / media elements and applications that are implemented on a single system or distributed across multiple systems. If distributed components are used, they can operate sequentially or parallel to each other.
    [0023] The historical parameter information contained in the database 16 contains data that reflect the operation, repairs and / or maintenance measures of the comparable gas turbines. The historical parameter information may specifically contain data which is referred to as load data and damage data. Stress data include any information describing the operational history of a comparable gas turbine and which can be statistically assigned to predicting a fault mode or mechanism. For example, stress data may include operating hours, number of start and shutdown cycles, firing temperatures, and the number of unscheduled releases. Damage data includes any hardware failure mechanisms that have occurred with a statistical significance. A fault mechanism includes any deterioration of the physical or functional characteristics against the nominal values ​​resulting in a reduction in output power, loss of efficiency or the inability to operate the comparable gas turbine. Examples of known defect mechanisms include corrosion, pitting, deformation, fatigue, damage from foreign objects, oxidation, thermal barrier coating (TBC), clogging / contamination, fracture, crack, and wear. These defect mechanisms can be detected or recorded as a result of enhanced boroscope inspections, on-site monitoring, operating protocols, repair logs, maintenance logs, and the like.
    [0024] The available sources of historical information include, for example, databases with operational experiences, operational records, parts inspection records and inspection reports in the field. Examples of the historical information contained in these sources include, but are not limited to, reports of enhanced boroscope inspections, electronic records, monitoring and diagnostic data, standstill events, operating times, starts and releases, and maintenance or repair data ,
    [0025] The collection of historical information, such as stress and damage data, is statistically analyzed and normalized to develop the fleet model, which is also referred to as a data accumulation model. The fleet model predicts parameter information, such as growth of damage, during future stresses using the acquired historical information, and the fleet model and / or the parameterized parameter information is transmitted to the processor 22 by the fleet model signal.
    [0026] The input device 34 allows a user to communicate with the system 20 and may include any structure to provide an interface between the user and the system 20. For example, the input device 34 may include a keyboard, computer, terminal, tape drive, and / or any other device for receiving input from a user and generating the flotation output signal 28, the plant data signal 30, and / or the risk signal 32 for the system 20 contain.
    [0027] FIG. 3 shows an algorithm for updating and validating the fleet model and / or the fleet model signal 24 referred to above in the form of the block 26 of FIG. In block 50, the algorithm imports the flotation data signal 28 which, for example, comprises newly acquired parameter information of comparable gas turbines in the fleet, for example, load data 52 and damage data 54. For purposes of illustration, it is assumed that the flotation data signal 28 indicates that during an operating time of 10,000 hours, 20 start and stop cycles, and two unscheduled trips, boroscope inspections in a particular component are cracks of sizes 0.1.0.2.0 , 1.0.2,0,3 and 0.2. In block 56, the algorithm sorts the imported stress data 52 and damage data 54 and organizes them by, for example, assigning a variable Ln to each inspection result in ascending order according to the amount of damage detected to produce the following result: L, = 0.1, L2 = 0.1, L3 = 0.2, L4 = 0.2, L5 = 0.2 and L6 = 0.3. In block 58, the algorithm groups the sorted stress data 52 and damage data 54, eg by assigning a variable FV, to each inspection result with the same amount to produce the following result: R-, = 2/6, R2 = 2/6, R3 = 3/6, R4 = 3/6, R5 = 3/6, and R6 = 1/6. In block 60, the algorithm compares the sorted and grouped data 52, 54 with the fleet model signal 24, the distribution parameter information, Such as, for example, the highly calculated damage results based on the fleet model, in order to determine whether the fleet model is statistically accurate. The statistical accuracy can be determined from many individual or combined statistical criteria including, for example, the value of the measure of consistency (R2) or the standard deviation (σ). If the comparison indicates that the fleet model provides a statistically accurate calculation of the actual damage, block 62, the algorithm then updates the database 26 of historical parameter information with the newly acquired parameter information of comparable gas turbines in the fleet, and provides the updated fleet model signal 33 for further analysis , The updated fleet model is the investment model, When accessed therefrom by the algorithm illustrated in FIG. If the comparison indicates that the fleet model does not provide a statistically accurate calculation of the actual damage, the algorithm then generates a flag 66 or other signal that indicates the need to examine the error between the fleet model high scores and the actual damage data.
    [0028] FIG. 4 shows an algorithm for updating and validating the plant model referred to above as the block 38 in FIG. In block 68, the algorithm imports the plant data signal 30 which, for example, comprises newly acquired parameter information from the gas turbine in the insert 10, for example, stress data 70 and damage data 72. For illustrative purposes only, it is assumed once again that the plant data signal 30 indicates that during an operating time of 10 000 hours, with 20 start and stop cycles and two unscheduled releases, boroscope inspections in a particular component have cracks of sizes 0.1, 0.3, 0.1, 0.3, 0.3, and 0.2. In block 74, the algorithm sorts the imported facility data 70, 72 and organizes them by, for example, assigning a variable Ln to each inspection result in ascending order according to the amount of the detected damage to produce the following result: L, = 0.1, L2 = 0.1, L3 = 0.2, L4 = 0.3, L5 = 0.3, and L6 = 0.3. In block 76, the algorithm groups the sorted facility data 70, 72 by assigning a variable Rn to each inspection result with the same amount to produce the following result: R-1 = 2/6, R2 = 2/6, R3 = 1 / 6, R4 = 3/6, R5 = 3/6, and R6 = 3/6. In block 78, the algorithm compares the assorted and grouped asset data 70, 72 with the asset model that contains distribution parameter information, such as the highly calculated damage results based on the asset model, Whether the investment model is statistically accurate. The statistical accuracy can be determined by a number of single or combined statistical criteria, including, for example, the value of the measure of consistency (R2) or the standard deviation (σ). If the comparison indicates that the plant model provides a statistically accurate calculation of the actual damage, block 80, the algorithm subsequently updates the plant model with the newly acquired parameter information from the gas turbine in the insert 10 and generates updated parameter information 41 from the plant model for further analysis. If the comparison indicates that the system model does not provide a statistically accurate calculation of the actual damage, the algorithm subsequently generates a flag 84 or other signal,
    [0029] FIG. 5 shows an algorithm for carrying out the risk analysis analysis referred to above in the form of the block 40 in FIG. 2. The stock risk analysis combines the updated parameter information 41 from the investment model with the risk signal 32 to produce the output signal 42 representing the plans for repair 44 and / or maintenance 46 and / or the service life expectancy 48 for the gas turbine in the insert 10 , In block 86, the algorithm imports the risk signal 32, which includes, for example, plant stress data, permissible risk levels for each fault mechanism, and / or the next maintenance interval for the gas turbine in the insert 10. In block 88, the algorithm imports the updated parameter information 41 from the plant model, For example, the distribution system parameter information, for example the highly calculated damage results based on the plant model. In block 90, the algorithm loads risk analysis equations or accesses risk analysis equations associated with each error mechanism. The risk analysis equations can use any of several techniques known in the art to model the distribution curves of future states based on known data. For example, the risk analysis equations can use a Weibull-Ioglinear model, Weibull-proportional damage model, or Lognormal-Ioglinear model. In block 90, the algorithm loads risk analysis equations or accesses risk analysis equations associated with each error mechanism. The risk analysis equations can use any of several techniques known in the art to model the distribution curves of future states based on known data. For example, the risk analysis equations can use a Weibull-Ioglinear model, Weibull-proportional damage model, or Lognormal-Ioglinear model. In block 90, the algorithm loads risk analysis equations or accesses risk analysis equations associated with each error mechanism. The risk analysis equations can use any of several techniques known in the art to model the distribution curves of future states based on known data. For example, the risk analysis equations can use a Weibull-Ioglinear model, Weibull-proportional damage model, or Lognormal-Ioglinear model.
    [0030] In block 92, the algorithm calculates a conditional risk associated with each particular error mechanism using the risk analysis equations. The conditional risk is the likelihood that an investment parameter will reach or exceed a predetermined limit at any time in the future. The predetermined parameter limit may be any state, any metric, any measure, or other criterion set by the user. For example, the predetermined parameter limit may be an operational limit value, such as a crack size, a part, or a component which, when exceeded, is a measure by the user such as performing an additional inspection, Removal of the part or component from the operation, repair of the part or component, or restriction of the performance of the gas turbine in the insert 10. *** " The time in the future may be the next inspection interval for the gas turbine in use 10 as measured chronologically by operating hours, starts, shutdowns, unscheduled trips, or any other load data provided by the user and related to the fault mechanism.
    In block 94, the algorithm calculates the operational reliability in the present state in the gas turbine in the insert 10. The calculated reliability is the probability that a part or a component will be able to perform the intended function (S) in the future, at least until a certain time in the future. In other words, the calculated reliability is the probability that a part or a component will not fail due to an identified fault mechanism before a certain time in the future. As with the calculation of the conditional risk, the time point in the future may be the next inspection interval for the gas turbine 10 in use as chronologically, based on operating hours, starts,
    [0032] In block 96, the algorithm calculates the remaining useful life for the part or component, and FIG. 6 shows an algorithm for performing this calculation. In blocks 98 and 100, the algorithm imports the risk signal 32 and the updated parameter information 41, respectively, as discussed above with respect to blocks 86 and 88 in FIG. In block 102, the algorithm calculates the average damage value for each particular fault mechanism for the gas turbine in the insert 10. In block 104, the algorithm calculates the probability that the part or component will exceed a predetermined operational limit at various future stress points (eg, operating hours, Disconnections, unplanned releases, etc.). In block 106, the algorithm calculates the most restrictive stress time on the basis of the allowable risk level as provided by the user for each error mechanism. If the user provides an allowable risk level of 5% for the crack size using the data presented in the previous examples for illustration, and the predetermined operational limit for the crack size is 0.5, the block 106 of the algorithm calculates the stress time at which the conditional Risk that a crack size of 0.5 will be present is 5%.
    [0033] Returning to FIG. 5, the attachment risk analysis algorithm generates the output signal 42 representing the results of the attachment risk analysis. For example, the output signal 42 may contain repair plans 44 and / or maintenance plans 46 and / or a service life calculation for the gas turbine in the insert 10 or a component therein.
    FIG. 7 graphically illustrates hypothetical damage curves which can be generated by the attachment risk analysis algorithm according to an embodiment of the present invention. The horizontal axis represents the load interval (eg, operating hours, startup events, shutdowns, unscheduled trips, or any other load data associated with a fault mechanism) between standstills for repair and / or maintenance, and the vertical axis represents the amount of damage to a part Or a component in the gas turbine in the insert 10. A horizontal line above the graph represents the predetermined parameter limit 110 or operational limit of a part or component as determined by the user.
    [0035] Each graph on the graph in FIG. 7 represents a hypothetical damage curve. For example, the curve labeled 112, according to the fleet model, reflects a risk of 5% for a part or a component which has no detected damage to exceed the predetermined parameter limit 110 before the stress interval indicated at 114. The curve denoted by 116 reflects a risk according to the fleet model of 95% for a part or a component without a detected damage exceeding the predetermined parameter limit 110 before the stress interval indicated at 118. The curve labeled 120 reflects a risk of 5% according to the updated fleet model or investment model, That a part or a component without a detected damage before the stress interval indicated at 122 will exceed the predetermined parameter limit 110. The curve denoted by 124 reflects a risk according to the updated fleet model or system model of 95% for a part or a component without a detected damage before the stress interval indicated at 126 exceeds the predetermined parameter limit 110. The various data points, denoted by 128, represent actual inspection results, which have been variously referred to above as plant parameter information or damage data 72, and which are transmitted to the processor 22 on the basis of the plant data signal 30. Referring again to FIG. 2, these damage data 72 are added to the plant model in block 38 to produce the updated parameter information 41. The stock risk analysis combines the updated parameter information 41 with information in the risk signal 32 to determine the actual risk curve for the gas turbine in the insert 10.
    FIG. 8 graphically illustrates hypothetical usage life curves generated by the algorithm as explained above in connection with FIG. 6. In this illustration, the horizontal axis represents the stress limit in operating hours and the vertical axis represents the stress limit for start operations. Other stress limits may be applicable depending on various factors such as the fault mechanism, the particular part or the specific component, the stress data for the gas turbine in the insert 10, etc. The curve labeled 130 represents a hypothetical usage life curve for a part or component for a particular fault mechanism. The point 132 represents an intended service life for a part or components for a given combination of starts and operating hours. The curve denoted by 134 represents a new useful life curve for the part or component as calculated by blocks 106 and 108 in FIG. As illustrated, the new usage life curve 134 shows the increased number of starts and operating hours that the part or component may have before the fault mechanism occurs.
    [0037] This description uses examples to disclose the invention, including the best mode, and also to enable anyone skilled in the art to practice the invention, including the creation and use of any devices or systems and the performance of any contained methods , The patentable scope of the invention is defined by the claims and may include other examples which may occur to those skilled in the art. Such further examples are intended to be within the scope of the claims if they contain structural elements which are not different from the wording of the claims, or if they contain equivalent structural elements with insignificant differences compared to the wording of the claims.
    A system 20 for monitoring a gas turbine 10 includes a database 26 containing information from comparable gas turbines and an input device 34 which generates a system data signal 30 and a risk signal 32. A processor 22 in communication with the memory and the input device 34 takes the attachment data signal 30 into the database 26, calculates information for the gas turbine 10, and calculates a conditional risk that the gas turbine 10 will reach a limit. An output signal 42 contains repair or maintenance schedules. A method for monitoring a gas turbine 10 includes receiving information from comparable gas turbines, Add information from the gas turbine 10 to the information of comparable gas turbines and projecting information for the gas turbine 10 into the future. The method further includes calculating a conditional risk that the gas turbine 10 will reach a limit and generating an output signal 42 containing repair or maintenance schedules.
    Reference numeral 10 Gas turbine 12 Compressor 14 Combustion chamber 16 Turbine 20 System 22 Processor 24 Fleet model signal 26 Database 28 Flotation data signal 30 System data signal 32 Risk signal 33 Updated fleet model signal 34 Input device 36 Fleet model verification block 38 System modeling block 40 Storage risk analysis block 41 Parameter information from the plant model 42 Output signal 44 Repair plan 46 Maintenance plan 48 Useful life calculation 50 Flotation data import block 52 Fleet load data 54 Fleet damage data 56 Flotation data sorting block 58 Flotation data grouping block 60 Fleet comparison block 62 Fleet updating block 66 Fleet flag block 68 System data import block 70 Application data 72 Ancillary damage data 74 System data sorting block 76 System data grouping block 78Asset comparison block 80 asset updating block 84 asset flag block 86 input block of the stock risk analysis 88 import block of the stock risk analysis 90 risk analysis equation loading block of the stock risk analysis 92 conditional risk monitoring block of the stock risk analysis 94 reliability calculation block of the stock risk analysis 96 utilization life block of the stock risk analysis 98 input block for the service life calculation 100 import block for the service life calculation 100 import block for the service life calculationOf the deposit risk analysis 98 Input block for the useful life calculation 100 Import block for the life expectancy calculationOf the deposit risk analysis 98 Input block for the useful life calculation 100 Import block for the life expectancy calculation
    权利要求:
    Claims (8)
    [1] 102 Calculation of the downtime for the duration of the life expectancy calculation 108 Calculation of the remaining useful life for the service life calculation 110 Operational limit 112 5% -Flat curve 114 5% -Flat limit 116 95% -Flat curve 118 95% -Flat limit 120 5% Plant Curve 122 5% Plant Limitation 124 95% Plant Curve 126 95% Plant Limitation 128 Plant Data 130 Hypothetical Life-Duration Curve 132 Provided for Life 134 Curve of the New Living Patents
    Anspruch [en] A system (20) for monitoring the behavior of a gas turbine in use (10) comprising: a) a storage element containing a database (26) with historical parameter information from comparable gas turbines; (30) which contains parameter information from the gas turbine in the insert (10), and a risk signal ( 32) which contains a risk benefit for the gas turbine in the insert (10), for example, application stress data, permissible risk levels for each fault mechanism and / or the next maintenance interval for the gas turbine in the insert (10); A processor (22) in communication with the memory element and the input device (34), (30) into the database (26) with parameter information from comparable gas turbines, calculates parameter information for the gas turbine in the insert (10) and calculates a conditional risk that the parameterized parameter information for the gas turbine is used. (DE) (10) will reach a predetermined parameter limit; And (c) an output signal (42) generated by the processor (22), the output signal (42) including at least either repair and / or maintenance planning information. The parameterized parameter information for the gas turbine in use (10) will reach a predetermined parameter limit; And (c) an output signal (42) generated by the processor (22), the output signal (42) including at least either repair and / or maintenance planning information. The parameterized parameter information for the gas turbine in use (10) will reach a predetermined parameter limit; And (c) an output signal (42) generated by the processor (22), the output signal (42) including at least either repair and / or maintenance planning information.
    [2] 2. The system according to claim 1, further comprising a fleet model signal transmitted from the database to the processor, the fleet model signal being generated by a fleet model contained in the database using the historical model Parameter information contains highly-calculated parameter information from comparable gas turbines.
    [3] 3. System (20) according to claim 1, wherein the database (26) contains historical parameter information from comparable gas turbine data, which at least reflect either the operation and / or repairs and / or maintenance measures of the comparable gas turbines.
    [4] 4. System (20) according to claim 1, wherein the system data signal (30) contains data which at least reflect either the operation and / or repairs and / or the maintenance of the gas turbine in the insert (10).
    [5] 5. System (20) according to one of claims 1-4, wherein the processor (22) generates the output signal (42) on the basis of a comparison of the conditional risk with the risk counter.
    [6] 6. The system as claimed in claim 1, wherein the output signal contains a high-estimated service life of a component in the gas turbine in the insert.
    [7] 7. A method for monitoring the behavior of a gas turbine in use (10) comprising: a) receiving historical parameter information from comparable gas turbines; B) adding parameter information from the gas turbine in use (10) to the parameter information of comparable gas turbines; C) Calculation of the parameter information for the gas turbine in use (10); D) calculating a conditional risk that the parameterized parameter information for the gas turbine in use (10) will reach a predetermined parameter limit; And e) generating an output signal (42) including at least one of a repair plan and a maintenance plan for the gas turbine in use (10) based on the conditional risk.
    [8] 8. The method according to claim 7, further comprising comparing the conditional risk with a predetermined risk weight.
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    同族专利:
    公开号 | 公开日
    DE102011000298A1|2011-12-15|
    CN102155301B|2014-11-26|
    CN102155301A|2011-08-17|
    CH702625A2|2011-08-15|
    CH702625A8|2011-12-15|
    US20110196593A1|2011-08-11|
    JP2011163345A|2011-08-25|
    US8370046B2|2013-02-05|
    JP5844978B2|2016-01-20|
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    法律状态:
    2011-11-30| PK| Correction|
    2011-12-15| PK| Correction|Free format text: ERFINDER BERICHTIGT. |
    2017-03-15| NV| New agent|Representative=s name: GENERAL ELECTRIC TECHNOLOGY GMBH GLOBAL PATENT, CH |
    优先权:
    申请号 | 申请日 | 专利标题
    US12/704,031|US8370046B2|2010-02-11|2010-02-11|System and method for monitoring a gas turbine|
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