CN116007951A - Fault diagnosis method and device for gas turbine - Google Patents
Fault diagnosis method and device for gas turbine Download PDFInfo
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Abstract
The present application relates to a fault diagnosis method and apparatus of a gas turbine, the fault diagnosis method comprising: receiving operational data of the gas turbine; comparing the operation data with the historical operation data, and judging whether the operation data has faults or not; and under the condition that the judgment result is yes, calculating a fault membership value of the operation data, and determining the fault type according to the fault membership value. The technical effect of rapid and objective fault diagnosis of the gas turbine in actual operation can be achieved by the scheme provided by the invention.
Description
Technical Field
The present disclosure relates to the field of gas turbine fault diagnosis, and in particular, to a fault diagnosis method and apparatus for a gas turbine.
Background
In the process of operating the gas turbine, many fault problems exist, and if an effective fault diagnosis technology cannot be adopted, the quick and accurate solution of the fault of the gas turbine is difficult to ensure, so that the research on the fault diagnosis technology of the gas turbine is very necessary.
The existing gas turbine fault diagnosis method generally adopts a fault mechanism analysis or big data modeling diagnosis method. However, the failure mechanism analysis often requires deeper theoretical knowledge, a large number of tests are required to verify the accuracy of the model, test data of specific failures often lack and cannot be confirmed, and the theoretical model often has deviation from the actual running condition due to various unit types; the method based on data modeling diagnosis often requires a large amount of data training, so that the dimension of the data amount is large, the calculated amount is large, the relevance between the data and the fault cannot be determined, and the fault diagnosis accuracy is low.
Aiming at the problems that in the prior art, the fault diagnosis of the gas turbine is mainly carried out by means of the diagnosis experience of an expert, the subjectivity risk is high, the dimension of the diagnosis data volume is large, the calculation amount is large, and the fault diagnosis accuracy is low, no effective solution is proposed at present.
Disclosure of Invention
The embodiment of the application provides a fault diagnosis method and device for a gas turbine, which are used for at least solving the problems that in the prior art, fault diagnosis of the gas turbine is mainly carried out by means of expert diagnosis experience, the subjectivity risk is high, the dimension of diagnostic data volume is large, the calculated amount is large, and the fault diagnosis accuracy is low.
In a first aspect, an embodiment of the present application provides a fault diagnosis method for a gas turbine, including: receiving operational data of the gas turbine; comparing the operation data with the historical operation data, and judging whether the operation data has faults or not; and under the condition that the judgment result is yes, calculating a fault membership value of the operation data, and determining the fault type according to the fault membership value.
Optionally, before receiving the operation data of the gas turbine, the method further comprises: acquiring a fault data set from pre-acquired historical operation data; acquiring a first change rate of each monitoring data before and after the gas turbine fails according to the failure data set; determining characteristic parameters corresponding to each fault according to the change rate; and determining the weight of the characteristic parameters of each fault and the fault membership threshold according to the second change rate of the characteristic parameters.
Further optionally, before acquiring the fault data set from the pre-acquired historical operating data, the method further comprises: and dividing faults in the historical operation data to obtain a fault minimum unit of the gas turbine, wherein the fault minimum unit is a minimum component of the gas turbine to be replaced or maintained after the faults occur.
Further, optionally, the historical operating data includes: a historical operation data set of normal operation of each working condition of the gas turbine and a fault data set of fault events; wherein, the operating mode includes: the load of the gas turbine unit, the temperature of the air entering the compressor and the compression ratio of the air in the compressor are all the same.
Optionally, obtaining the first rate of change of each of the monitoring data before and after the gas turbine fails according to the failure data set includes:
wherein ,Ai Representing the value, delta, of the monitored parameter i i Indicating that the occurrence of a specified type of fault is the rate of change of parameter i, A i0 An initial value representing a parameter before failure; a is that imax Indicating the maximum value reached by the parameter after the fault occurs; t represents the time for which the fault reaches a maximum.
Further, optionally, determining the weight of the feature parameter of each fault and the fault membership threshold includes:
determining the weight of the characteristic parameters of each fault comprises:
wherein ,wF1 +w F2 +····+w Fj ＝1；
Determining a fault membership threshold of the characteristic parameters of each fault comprises:
wherein ,
when the fault F occurs, a fault membership threshold value of the characteristic parameter is represented; taking the fault F to happen several times +.>
The minimum value of (2) as the fault membership threshold of the fault characteristic parameter is expressed as +.>
Optionally, determining the fault type according to the fault membership value includes: judging whether the fault membership value is larger than or equal to a fault membership threshold value of the characteristic parameters of each fault; if the judgment result is yes, determining that the fault of the gas turbine is the fault to which the fault membership threshold value belongs; and if the judging result is negative, determining that the fault of the gas turbine is not the fault to which the fault membership threshold belongs.
In a second aspect, embodiments of the present application provide a fault diagnosis apparatus for a gas turbine, including: a receiving module for receiving operational data of the gas turbine; the comparison module is used for comparing the operation data with the historical operation data and judging whether the operation data has faults or not; and the type determining module is used for calculating a fault membership value of the operation data under the condition that the judging result is yes, and determining the fault type according to the fault membership value.
Optionally, the apparatus further comprises: an acquisition module for acquiring a failure data set from previously acquired historical operation data before receiving the operation data of the gas turbine; the first calculation module is used for acquiring a first change rate of each monitoring data before and after the gas turbine fails according to the failure data set; the second calculation module is used for determining characteristic parameters corresponding to each fault according to the change rate; and the third calculation module is used for determining the weight of the characteristic parameters of each fault and the fault membership threshold according to the second change rate of the characteristic parameters.
Further optionally, the apparatus further comprises: the division module is used for dividing faults in the historical operation data before the fault data set is acquired from the pre-acquired historical operation data to obtain a fault minimum unit of the gas turbine, wherein the fault minimum unit is a minimum component of the gas turbine to be replaced or maintained after the fault occurs.
The embodiment of the invention provides a fault diagnosis method and device for a gas turbine. Receiving operational data of the gas turbine; comparing the operation data with the historical operation data, and judging whether the operation data has faults or not; under the condition that the judgment result is yes, calculating a fault membership value of the operation data, determining a fault type according to the fault membership value, and only monitoring a few parameters to truly reflect a specific fault, so that the number of data processing is reduced; meanwhile, the fault diagnosis is performed according to the rapid quantification of the actual operation data, so that subjective risks of the fault diagnosis by means of expert diagnosis experience are avoided, and the technical effect of rapid and objective fault diagnosis of the gas turbine in the actual operation can be achieved.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiments of the application and together with the description serve to explain the application and do not constitute an undue limitation to the application. In the drawings:
FIG. 1 is a schematic flow chart of a method for diagnosing a fault of a gas turbine according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a method for diagnosing a fault of a gas turbine according to an embodiment of the present invention;
fig. 3 is a schematic diagram of a fault diagnosis apparatus for a gas turbine according to a second embodiment of the present invention.
Detailed Description
In order that those skilled in the art will better understand the present invention, a technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in which it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present invention without making any inventive effort, shall fall within the scope of the present invention.
It should be noted that the terms "first," "second," and the like in the description and the claims of the present invention and in the drawings are used for distinguishing between different objects and not for limiting a particular order.
It should be noted that, the following embodiments of the present invention may be implemented separately or in combination with each other, and the embodiments of the present invention are not limited thereto.
Example 1
In a first aspect, an embodiment of the present invention provides a method for diagnosing a fault of a gas turbine, and fig. 1 is a schematic flow chart of a method for diagnosing a fault of a gas turbine according to a first embodiment of the present invention; as shown in fig. 1, the fault diagnosis method of the gas turbine provided in the embodiment of the application includes:
step S102, receiving operation data of a gas turbine;
optionally, before step S102 receives operation data of the gas turbine, the fault diagnosis method of the gas turbine provided in the embodiment of the present application further includes: acquiring a fault data set from pre-acquired historical operation data; acquiring a first change rate of each monitoring data before and after the gas turbine fails according to the failure data set; determining characteristic parameters corresponding to each fault according to the change rate; and determining the weight of the characteristic parameters of each fault and the fault membership threshold according to the second change rate of the characteristic parameters.
Specifically, fig. 2 is a schematic structural diagram of a fault diagnosis method of a gas turbine according to a first embodiment of the present invention, as shown in fig. 2, in the fault diagnosis method of a gas turbine according to the embodiment of the present invention, the obtaining a fault data set from pre-obtained historical operation data may specifically be: collecting historical operation data to form a historical operation data set of normal operation of each working condition and a fault data set of known fault events;
wherein the historical operating data comprises: a historical operation data set of normal operation of each working condition of the gas turbine and a fault data set of fault events; wherein, the operating mode includes: the load of the gas turbine unit, the temperature of the air entering the compressor and the compression ratio of the air in the compressor are all the same.
Specifically, the working condition of the gas turbine refers to the running state that the unit load, the temperature of air entering the compressor and the compression ratio of the air in the compressor are the same; and collecting the normal operation and the known fault data of the historical occurrence to form a historical operation data set of the normal operation of each working condition and a fault data set of the known fault event, wherein the collected normal operation data and the collected fault data are all data which can be monitored at the moment, and data omission is avoided.
Optionally, obtaining the first rate of change of each of the monitoring data before and after the gas turbine fails according to the failure data set includes:
wherein ,Ai Representing the value, delta, of the monitored parameter i i Indicating that the occurrence of a specified type of fault is the rate of change of parameter i, A i0 An initial value representing a parameter before failure; a is that imax Indicating the maximum value reached by the parameter after the fault occurs; t represents the time for which the fault reaches a maximum.
Specifically, according to a fault data set of a fault event, the change rate of each monitoring data before and after the occurrence of the fault is obtained, and the characteristic parameters of each fault are determined according to the change rate.
In a preferred example, when an air filter failure occurs, no more than 10 parameters may be generally selected as characteristic parameters of the air filter failure according to the ordering of the rates of change of the parameters, the characteristic parameters of other failure types, and so on.
Further, optionally, determining the weight of the feature parameter of each fault and the fault membership threshold includes:
determining the weight of the characteristic parameters of each fault comprises:
wherein ,wF1 +w F2 +····+w Fj ＝1；
When there are j feature parameters for the fault, the calculation method of the feature parameter weights is as described above.
Determining a fault membership threshold of the characteristic parameters of each fault comprises:
wherein ,
when the fault F occurs, a fault membership threshold value of the characteristic parameter is represented; taking the fault F to happen several times +.>
The minimum value of (2) as the fault membership threshold of the fault characteristic parameter is expressed as +.>
The fault diagnosis method of the gas turbine provided by the embodiment of the application is used for guaranteeing the safety of a unit, and generally, when the fault F occurs for a plurality of times
Is expressed as the membership threshold of the fault characteristic parameter
Further, optionally, before acquiring the fault data set from the previously acquired historical operation data, the fault diagnosis method of the gas turbine provided by the embodiment of the application further includes: and dividing faults in the historical operation data to obtain a fault minimum unit of the gas turbine, wherein the fault minimum unit is a minimum component of the gas turbine to be replaced or maintained after the faults occur.
The smallest component to be replaced or repaired may be in the gas turbine: compressor blade failure, turbine blade failure, bearing failure, air filter failure, rotor misalignment failure, rotor imbalance failure, etc.
Step S104, comparing the operation data with the historical operation data, and judging whether the operation data has faults or not;
and S106, under the condition that the judgment result is yes, calculating a fault membership value of the operation data, and determining the fault type according to the fault membership value.
The determining the fault type according to the fault membership value in step S106 includes: judging whether the fault membership value is larger than or equal to a fault membership threshold value of the characteristic parameters of each fault; if the judgment result is yes, determining that the fault of the gas turbine is the fault to which the fault membership threshold value belongs; and if the judging result is negative, determining that the fault of the gas turbine is not the fault to which the fault membership threshold belongs.
Specifically, judging whether the operation data has faults or not; under the condition that the judgment result is yes, calculating a fault membership value of the operation data, determining a fault type according to the fault membership value, thereby completing the rapid fault diagnosis of the gas turbine, realizing the feature parameter dimension reduction of each fault, actually reflecting the specific fault by monitoring a few parameters, and reducing the data processing quantity; meanwhile, fault diagnosis is performed according to the rapid quantification of the actual operation data, so that subjective risks of fault diagnosis by means of expert diagnosis experience are avoided.
When a gas turbine fails in a history which does not occur, and the gas turbine is compared with a history operation data set of normal operation of the working condition to exceed an alarm value, the fault type is checked through overhaul after the diagnosis expert judges, and then the characteristic parameters and the fault membership threshold value of the fault are determined again, so that the gas turbine is convenient to apply in subsequent fault diagnosis.
According to the fault diagnosis method for the gas turbine, which is provided by the embodiment of the application, the fault type of the gas turbine is quantitatively determined through the fault membership value, the gas turbine is rapidly subjected to fault diagnosis in actual operation, specific faults can be truly reflected only by monitoring a few parameters, the dimension reduction of the characteristic parameters of each fault is realized, and the number of data processing is reduced; meanwhile, the fault diagnosis is performed according to the rapid quantification of the actual operation data, so that subjective risks of the fault diagnosis by means of expert diagnosis experience are avoided, and the technical effect of rapid and objective fault diagnosis of the gas turbine in the actual operation can be achieved.
Example two
In a second aspect, an embodiment of the present invention provides a fault diagnosis device for a gas turbine, and fig. 3 is a schematic diagram of a fault diagnosis device for a gas turbine provided in a second embodiment of the present invention, as shown in fig. 3, where the fault diagnosis device for a gas turbine provided in the embodiment of the present invention includes: a receiving module 32 for receiving operational data of the gas turbine; a comparison module 34, configured to compare the operation data with the historical operation data, and determine whether the operation data has a fault; and the type determining module 36 is configured to calculate a fault membership value of the operation data and determine a fault type according to the fault membership value if the determination result is yes.
Optionally, the fault diagnosis device for a gas turbine provided in the embodiment of the present application further includes: an acquisition module for acquiring a failure data set from previously acquired historical operation data before receiving the operation data of the gas turbine; the first calculation module is used for acquiring a first change rate of each monitoring data before and after the gas turbine fails according to the failure data set; the second calculation module is used for determining characteristic parameters corresponding to each fault according to the change rate; and the third calculation module is used for determining the weight of the characteristic parameters of each fault and the fault membership threshold according to the second change rate of the characteristic parameters.
Further, optionally, the fault diagnosis device for a gas turbine provided in the embodiment of the present application further includes: the division module is used for dividing faults in the historical operation data before the fault data set is acquired from the pre-acquired historical operation data to obtain a fault minimum unit of the gas turbine, wherein the fault minimum unit is a minimum component of the gas turbine to be replaced or maintained after the fault occurs.
The embodiment of the invention provides a fault diagnosis device of a gas turbine. Receiving operational data of the gas turbine; comparing the operation data with the historical operation data, and judging whether the operation data has faults or not; under the condition that the judgment result is yes, calculating a fault membership value of the operation data, determining a fault type according to the fault membership value, and only monitoring a few parameters to truly reflect a specific fault, so that the number of data processing is reduced; meanwhile, the fault diagnosis is performed according to the rapid quantification of the actual operation data, so that subjective risks of the fault diagnosis by means of expert diagnosis experience are avoided, and the technical effect of rapid and objective fault diagnosis of the gas turbine in the actual operation can be achieved.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The foregoing description is only of the preferred embodiments of the present invention, and is not intended to limit the scope of the present invention.
Claims (10)
1. A method for diagnosing a fault in a gas turbine, comprising:
receiving operational data of the gas turbine;
comparing the operation data with historical operation data, and judging whether the operation data has faults or not;
and under the condition that the judgment result is yes, calculating a fault membership value of the operation data, and determining the fault type according to the fault membership value.
2. The method for diagnosing a fault in a gas turbine according to claim 1, wherein prior to said receiving operation data of said gas turbine, said method further comprises:
acquiring a fault data set from pre-acquired historical operation data;
acquiring a first change rate of each monitoring data before and after the gas turbine fails according to the failure data set;
determining characteristic parameters corresponding to each fault according to the change rate;
and determining the weight of the characteristic parameters of each fault and a fault membership threshold according to the second change rate of the characteristic parameters.
3. The method for diagnosing a fault in a gas turbine according to claim 2, wherein before said acquiring a fault data set from previously acquired historical operating data, the method further comprises:
and dividing faults in the historical operation data to obtain a fault minimum unit of the gas turbine, wherein the fault minimum unit is a minimum component of the gas turbine to be replaced or maintained after the faults occur.
4. The method for diagnosing a fault in a gas turbine according to claim 2, wherein the historical operating data includes: a historical operation data set of normal operation of each working condition of the gas turbine and a fault data set of fault events; wherein, the operating mode includes: the load of the gas turbine unit, the temperature of the air entering the compressor and the compression ratio of the air in the compressor are all the same.
5. The method of diagnosing a gas turbine according to claim 2, wherein the acquiring a first rate of change of each of the monitoring data before and after the gas turbine fails according to the failure data set includes:
wherein ,Ai Representing the value, delta, of the monitored parameter i i Indicating that the occurrence of a specified type of fault is the rate of change of parameter i, A i0 An initial value of the parameter before the fault occurs; a is that imax Indicating the maximum value reached by the parameter after the fault occurs; t represents the time for which the fault reaches a maximum.
6. The method of diagnosing a fault in a gas turbine according to claim 2, wherein said determining a weight and a fault membership threshold for said characteristic parameter of each fault includes:
determining the weight of the characteristic parameter of each fault comprises:
wherein ,wF1 +w F2 +····+w Fj ＝1；
Determining a fault membership threshold for the characteristic parameters of each fault includes:
wherein ,
when the fault F occurs, the fault membership threshold value of the characteristic parameter is represented; taking the fault F to happen several times +.>
Is expressed as +.>
7. The method of diagnosing a fault in a gas turbine according to claim 6, wherein said determining a fault type based on said fault membership value includes:
judging whether the fault membership value is larger than or equal to a fault membership threshold value of the characteristic parameters of each fault;
if the judgment result is yes, determining that the fault of the gas turbine is the fault to which the fault membership threshold value belongs;
and if the judging result is negative, determining that the fault of the gas turbine is not the fault to which the fault membership threshold belongs.
8. A fault diagnosis apparatus for a gas turbine, comprising:
a receiving module for receiving operational data of the gas turbine;
the comparison module is used for comparing the operation data with the historical operation data and judging whether the operation data has faults or not;
and the type determining module is used for calculating the fault membership value of the operation data under the condition that the judging result is yes, and determining the fault type according to the fault membership value.
9. The fault diagnosis device for a gas turbine according to claim 8, further comprising:
an acquisition module for acquiring a fault data set from previously acquired historical operating data prior to said receiving operating data of said gas turbine;
the first calculation module is used for acquiring a first change rate of each monitoring data before and after the gas turbine fails according to the failure data set;
the second calculation module is used for determining characteristic parameters corresponding to each fault according to the change rate;
and the third calculation module is used for determining the weight of the characteristic parameters and the fault membership threshold value of each fault according to the second change rate of the characteristic parameters.
10. The fault diagnosis device for a gas turbine according to claim 9, further comprising:
the division module is used for dividing faults in the historical operation data before the fault data set is acquired from the pre-acquired historical operation data to obtain a fault minimum unit of the gas turbine, wherein the fault minimum unit is a minimum component of the gas turbine to be replaced or maintained after the fault occurs.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN117992875A (en) * | 2024-04-07 | 2024-05-07 | 杭州汽轮动力集团股份有限公司 | Gas turbine fault diagnosis method and device |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN117992875A (en) * | 2024-04-07 | 2024-05-07 | 杭州汽轮动力集团股份有限公司 | Gas turbine fault diagnosis method and device |
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