CN109508745A - The detection method of gas turbine gascircuit fault based on Bayesian network model - Google Patents
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Abstract
A kind of detection method of the gas turbine gascircuit fault based on Bayesian network model, the data for the gas turbine gas path component that real time signal aquisition is obtained generate data set, nominal situation parameter is obtained from data set, anomaly parameter to be measured, training set and test set, preprocessed and clustering obtains the optimization training set and optimal inspection collection after discretization, then the Bayesian network after initialized and parameter optimization is tested by optimal inspection collection, obtain the optimization Bayesian network model of the work condition state current for real-time detection gas turbine air-channel system, to be detected to the system failure.The invention proposes the methods of specific Bayesian network model Structure learning and parameter learning, and then establish the correlation model between measurement parameter and gas turbine nominal situation parameter, realize the online fault detection of gas turbine air-channel system.
Description
Technical field
The present invention relates to a kind of technology of field of thermal power, specifically a kind of combustion based on Bayesian network model
The detection method of gas-turbine gas path failure.
Background technique
Structure is complicated for gas turbine, and longtime running is in the state of high revolving speed, high temperature and pressure and high stress, building ring
Border is severe, Yi Fasheng mechanical breakdown and gas path failure.Gas turbine is made of gas path component and subsystem, wherein gas turbine
If gas path component breaks down, it will seriously affect the availability of gas turbine, it is therefore desirable to the gas path component of gas turbine
Fault detection is carried out, discovering device is abnormal in advance, the major accident caused by escalation of fault is effectively prevent, to guarantee combustion gas
Turbine safe and stable operation.For gas turbine, gas path failure occurs generally in the form of single failure, is in normal in engine
When working condition, each component and complete machine have the corresponding feature of unfaulty conditions, and thermal parameter is in normal range；It is working
When breaking down in the case that environment is constant, the variation of component capabilities will lead to the variation of measurement parameter.Therefore, it is surveyed by analysis
The variation of amount parameter can estimate the variation of gas turbine nominal situation parameter, and then analyze the nominal situation of gas turbine gas circuit
Situation.The prior art can only mostly detect fault mode, or lack targetedly Structure learning and parametric learning method, not from
The actual demand of turbine engine failure detection is set out.
Summary of the invention
The present invention In view of the above shortcomings of the prior art, proposes a kind of gas turbine based on Bayesian network model
The detection method of gas path failure establishes the correlation model between measurement parameter and gas turbine nominal situation parameter, realizes combustion gas
The online fault detection of turbine air-channel system.
The present invention is achieved by the following technical solutions:
The data for the gas turbine gas path component that the present invention obtains real time signal aquisition generate data set, from data set
Nominal situation parameter, anomaly parameter to be measured, training set and test set are obtained, after preprocessed and clustering obtains discretization
Optimize training set and optimal inspection collection, then by optimal inspection collection to the Bayesian network after initialized and parameter optimization
It is tested, obtains the optimization Bayesian network model of the work condition state current for real-time detection gas turbine air-channel system,
To be detected to the system failure.
The data set operates normally data and fault data comprising multiple period gas turbine gas path components, through drawing
Get nominal situation parameter, anomaly parameter to be measured.
The data that the training set and test set is randomly selected in nondimensionalization treated data set, are preferably instructed
Practice and integrates with the ratio of test set as 4:1.
The pretreatment includes: cleaning exceptional value to delete invalid data, iterative median filter to reduce measuring instrument
Noise and combination gradient and the edge detection of Laplacian operator are to reduce wrong early warning.
The clustering refers to: being based on nominal situation parameter and anomaly parameter to be measured to instruction using K-Means algorithm
Practice collection and carry out continuous variable clustering, make training set discretization and obtain optimization training set and optimal inspection collection, specifically includes
Following steps are as follows:
A. k sample is randomly selected from data set as initial cluster centre C={ c1, c2 ..., ck }；
B. for data sets in each sample, calculate the distance for arriving k cluster centre, and it is nearest to assign it to distance
Cluster centre corresponding in class；
C. after all samples are assigned, the center of k cluster is recalculated；
D. k cluster centre being calculated for the second time is calculated gained with first time to be compared, if cluster centre is sent out
Changing then jumps to step b, and otherwise algorithm terminates.
The Bayesian network, using being based on, genetic algorithm is combined with K2 (GA-K2) algorithm and Bayesian Information is quasi-
Then the method for (BIC) is established to obtain, specifically includes the following steps:
I. initial population is generated, each individual is a kind of ordering structure of all-network node；
Ii. K2 algorithm is implemented to each of initial population individual, learns corresponding bayesian network structure out；
Iii. BIC scoring is carried out under current data set to the network structure that step ii is obtained, the expression formula of BIC scoring isWherein: first item is metrology structure model ζ and dataFitting
Degree, Section 2 are the penalty terms of structural model complexity；
Iiii. it regard BIC scoring as fitness function, is selected in original population, generate next-generation new population；
Iiiii. judge whether to reach default the number of iterations, be, carry out in next step, otherwise return step ii；
Iiiiii. the highest individual of fitness in current population is obtained final as node sequence with K2 algorithm
Bayesian network structure.
The initialization refers to: optimization training set being based on historical experience and expertise is modeled to know expert
Knowledge is converted into prior probability distribution, specifically includes the following steps:
1) Posterior probability distribution p (θ | D) is calculated based on data set；
2) the conditional probability distribution p (D of next sample is calculatedm+1|D)。
The parameter optimization refers to: after generating analog sample using importance sampling method, recycling Bayesian Estimation whole
Priori knowledge optimization network parameter study is closed, specifically includes the following steps:
1., according to network condition probability tables, n are obtained by way of generating random number mutually independently from root node
Sample；
2. approximate calculation Posterior Mean；
3. the conditional probability θ between network node is considered as stochastic variable, it is assumed that θ obeys known distribution p (θ), finds out survey
The sample Joint Distribution of the data set D obtained
4. Posterior distrbutionp p (θ | D) can be calculated according to the distribution of θ；
5. calculating p (x | D)=∫ p (x | θ) p (θ | D) d θ according to Bayesian Estimation formula；
6. p (x | D) is substituted into posterior probability p (wi| x, D) to obtain the optimal estimation of conditional probability θ.
The test refers to: carrying out Bayesian network model test based on optimal inspection collection, output infers accuracy rate most
Excellent Bayesian network model.
Technical effect
Compared with prior art, the invention proposes the sides of specific Bayesian network model Structure learning and parameter learning
Method, and then the correlation model between measurement parameter and gas turbine nominal situation parameter is established, realize gas turbine gas circuit system
The online fault detection of system.
Detailed description of the invention
Fig. 1 is the principle of the present invention figure；
Fig. 2 is schematic diagram of the invention；
Fig. 3 is present system schematic diagram.
Specific embodiment
As shown in figure 3, being a kind of gas turbine gascircuit fault based on Bayesian network model that the present embodiment is related to
Detection system, comprising: signal acquisition module, Bayesian network training module and Bayesian network test module, in which: signal
The data for the gas turbine gas path component that acquisition module input collects in real time, Bayesian network training module and signal acquisition
Module is connected and optimization training set and optimal inspection collection after transmitting discrete, Bayesian network test module and Bayesian network
Training module is connected and transmits the Bayesian network after initialized and parameter optimization, and the output of Bayesian network test module pushes away
The optimal Bayesian network model of disconnected accuracy rate.
As depicted in figs. 1 and 2, the data for the gas turbine gas path component that the present embodiment obtains real time signal aquisition generate
Data set obtains nominal situation parameter, anomaly parameter to be measured, training set and test set, preprocessed and cluster from data set
Analysis obtains the optimization training set and optimal inspection collection after discretization, then by optimal inspection collection to initialized and parameter
Bayesian network after optimization is tested, and the excellent of the work condition state current for real-time detection gas turbine air-channel system is obtained
Change Bayesian network model, to detect to the system failure.
The data set acquires multiple period gas turbine gas path components and operates normally data and fault data, altogether
31608 datas, it is divided to obtain nominal situation parameter, anomaly parameter to be measured, in which: the data of normal operation are divided into normally
There are three duty parameters, and fault data, which is divided into measurement parameter, eight.
The data that the training set and test set is randomly selected in nondimensionalization treated data set, are preferably instructed
Practice and integrates with the ratio of test set as 4:1.
The nominal situation parameter includes: flow factor SW, efficiency factor SE and leaving area SA.
The measurement parameter includes: three temperature T1~T3, three pressure P1~P3, low-pressure shaft revolving speed XNLP and high-pressure shaft
Revolving speed XNHP.
The training set refers to: randomly selecting from the data through nondimensionalization treated gas turbine gas path component
80% data, totally 25286.
The test set refers to: remaining 20% data after training set composition, totally 6322.
The pretreatment includes: cleaning exceptional value to delete invalid data, iterative median filter to reduce measuring instrument
Noise and combination gradient and the edge detection of Laplacian operator are to reduce wrong early warning.
The clustering refers to: being based on nominal situation parameter and anomaly parameter to be measured to instruction using K-Means algorithm
Practice collection and carry out continuous variable clustering, make training set discretization and obtain optimization training set and optimal inspection collection, specifically includes
Following steps are as follows:
A. k sample is randomly selected from data set as initial cluster centre C={ c1, c2 ..., ck }；
B. for data sets in each sample, calculate the distance for arriving k cluster centre, and it is nearest to assign it to distance
Cluster centre corresponding in class；
C. after all samples are assigned, the center of k cluster is recalculated；
D. k cluster centre being calculated for the second time is calculated gained with first time to be compared, if cluster centre is sent out
Changing then jumps to step b, and otherwise algorithm terminates.
The foundation of the bayesian network structure is combined with K2 (GA-K2) algorithm and shellfish using based on genetic algorithm
The method of this information criterion (BIC) of leaf, specifically includes the following steps:
I. initial population is generated, each individual is a kind of ordering structure of all-network node；
Ii. K2 algorithm is implemented to each of initial population individual, learns corresponding bayesian network structure out；
Iii. BIC scoring is carried out under current data set to the network structure that step ii is obtained, the expression formula of BIC scoring isWherein: first item is metrology structure model ζ and dataFitting
Degree, Section 2 are the penalty terms of structural model complexity；
Iiii. it regard BIC scoring as fitness function, is selected in original population, generate next-generation new population；
Iiiii. judge whether to reach default the number of iterations, be, carry out in next step, otherwise return step ii；
Iiiiii. the highest individual of fitness in current population is obtained final as node sequence with K2 algorithm
Bayesian network structure.
The described initialization modeling be the training set after discretization is based on to historical experience and expertise model with
Prior probability distribution is converted by expertise, specifically includes the following steps:
1) Posterior probability distribution p (θ | D) is calculated based on data set；
2) the conditional probability distribution p (D of next sample is calculatedm+1|D)。
The Bayesian network parameters optimization refers to: after generating analog sample using importance sampling method, recycling shellfish
Priori knowledge optimization network parameter study is integrated in Ye Si estimation, specifically includes the following steps:
1., according to network condition probability tables, n are obtained by way of generating random number mutually independently from root node
Sample；
2. approximate calculation Posterior Mean；
3. the conditional probability θ between network node is considered as stochastic variable, it is assumed that θ obeys known distribution p (θ), finds out survey
The sample Joint Distribution of the data set D obtained
4. Posterior distrbutionp p (θ | D) can be calculated according to the distribution of θ；
5. calculating p (x | D)=∫ p (x | θ) p (θ | D) d θ according to Bayesian Estimation formula；
6. p (x | D) is substituted into posterior probability p (wi| x, D) to obtain the optimal estimation of conditional probability θ.
The test refers to: carrying out Bayesian network model test based on optimal inspection collection, output infers accuracy rate most
Excellent Bayesian network model.
The dynamic real-time update is to be updated the information newly collected obtained to network model.
It is described that carry out Bayesian network model test to test set be 6322 datas using test set to training
Bayesian model tested, since nominal situation parameter is three, infer the dimension of accuracy rate for three-dimensional, such as the following table 1 institute
Show, obtain following result:
| Infer accurate number | Infer accuracy rate | |
| At least one is correct | 6322 | 100.0% |
| At least two is correct | 6233 | 98.6% |
| At least three is correct | 6091 | 96.3% |
It is obtained by upper table, all infers correct probability up to 96.3%.
Above-mentioned specific implementation can by those skilled in the art under the premise of without departing substantially from the principle of the invention and objective with difference
Mode carry out local directed complete set to it, protection scope of the present invention is subject to claims and not by above-mentioned specific implementation institute
Limit, each implementation within its scope is by the constraint of the present invention.
Claims (10)
1. a kind of detection method of the gas turbine gascircuit fault based on Bayesian network model, which is characterized in that will believe in real time
The data of number gas turbine gas path component collected generate data set, and nominal situation parameter, to be measured is obtained from data set
Anomaly parameter, training set and test set, preprocessed and clustering obtain the optimization training set and optimal inspection after discretization
Collection, then tests the Bayesian network after initialized and parameter optimization by optimal inspection collection, obtains for real
When the current work condition state of detection gas turbine air-channel system optimization Bayesian network model, to be examined to the system failure
It surveys.
2. the detection method of the gas turbine gascircuit fault according to claim 1 based on Bayesian network model, special
Sign is the data set, operates normally data and fault data comprising multiple period gas turbine gas path components, divided to obtain
To nominal situation parameter, anomaly parameter to be measured.
3. the detection method of the gas turbine gascircuit fault according to claim 1 based on Bayesian network model, special
Sign is, the data that the training set and test set are randomly selected in nondimensionalization treated data set are preferably trained
Integrate and the ratio of test set is 4:1.
4. the detection method of the gas turbine gascircuit fault according to claim 1 based on Bayesian network model, special
Sign is that the pretreatment includes: cleaning exceptional value to delete invalid data, iterative median filter to reduce measuring instrument noise
And combine gradient and the edge detection of Laplacian operator to reduce wrong early warning.
5. the detection method of the gas turbine gascircuit fault according to claim 1 based on Bayesian network model, special
Sign is that the clustering refers to: being based on nominal situation parameter and anomaly parameter to be measured to training set using K-Means algorithm
Continuous variable clustering is carried out, training set discretization is made and obtains optimization training set and optimal inspection collection.
6. the detection method of the gas turbine gascircuit fault according to claim 1 based on Bayesian network model, special
Sign is the Bayesian network, is combined with K2 (GA-K2) algorithm and bayesian information criterion using based on genetic algorithm
(BIC) method is established to obtain.
7. the detection method of the gas turbine gascircuit fault according to claim 1 based on Bayesian network model, special
Sign is that the initialization refers to: optimization training set being based on historical experience and expertise and is modeled with by expertise
It is converted into prior probability distribution.
8. the detection method of the gas turbine gascircuit fault according to claim 1 based on Bayesian network model, special
Sign is that the parameter optimization refers to: after generating analog sample using importance sampling method, recycling Bayesian Estimation integration first
Test the study of knowledge optimization network parameter.
9. the detection method of the gas turbine gascircuit fault according to claim 1 based on Bayesian network model, special
Sign is that the test refers to: carrying out Bayesian network model test based on optimal inspection collection, output infers that accuracy rate is optimal
Bayesian network model.
10. the present invention relates to a kind of systems for realizing the above method, comprising: signal acquisition module, Bayesian network training module
And Bayesian network test module, in which: the gas turbine gas path component that signal acquisition module input collects in real time
Data, Bayesian network training module is connected with signal acquisition module and optimization training set and optimal inspection after transmitting discrete
Collection, Bayesian network test module are connected with Bayesian network training module and transmit the shellfish after initialized and parameter optimization
The optimal Bayesian network model of accuracy rate is inferred in this network of leaf, the output of Bayesian network test module.
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Cited By (8)
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| CN110147849A (en) * | 2019-05-24 | 2019-08-20 | 吉林大学 | Electric control system of diesel engine weak link recognition methods under a kind of strong electromagnetic pulse |
| CN110162016A (en) * | 2019-05-30 | 2019-08-23 | 华北电力大学 | A kind of fault modeling method of gas turbine pneumatic actuator |
| CN110348094A (en) * | 2019-07-01 | 2019-10-18 | 中国人民解放军国防科技大学 | Petroleum pipeline leakage detection method and system based on influence network |
| CN110443344A (en) * | 2019-06-20 | 2019-11-12 | 中国地质大学（武汉） | A kind of momenttum wheel method for diagnosing faults and device based on K2ABC algorithm |
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| CN110147849A (en) * | 2019-05-24 | 2019-08-20 | 吉林大学 | Electric control system of diesel engine weak link recognition methods under a kind of strong electromagnetic pulse |
| CN110147849B (en) * | 2019-05-24 | 2022-03-25 | 吉林大学 | Method for identifying weak link of diesel engine electric control system under strong electromagnetic pulse |
| CN110162016A (en) * | 2019-05-30 | 2019-08-23 | 华北电力大学 | A kind of fault modeling method of gas turbine pneumatic actuator |
| CN110443344A (en) * | 2019-06-20 | 2019-11-12 | 中国地质大学（武汉） | A kind of momenttum wheel method for diagnosing faults and device based on K2ABC algorithm |
| CN110443344B (en) * | 2019-06-20 | 2022-05-06 | 中国地质大学（武汉） | Momentum wheel fault diagnosis method and device based on K2ABC algorithm |
| CN110348094A (en) * | 2019-07-01 | 2019-10-18 | 中国人民解放军国防科技大学 | Petroleum pipeline leakage detection method and system based on influence network |
| CN110348094B (en) * | 2019-07-01 | 2023-01-06 | 中国人民解放军国防科技大学 | Petroleum pipeline leakage detection method and system based on influence network |
| CN111091278A (en) * | 2019-12-04 | 2020-05-01 | 湃方科技(天津)有限责任公司 | Edge detection model construction method and device for mechanical equipment anomaly detection |
| CN111091278B (en) * | 2019-12-04 | 2023-09-08 | 湃方科技(天津)有限责任公司 | Edge detection model construction method and device for mechanical equipment anomaly detection |
| CN111311059A (en) * | 2020-01-16 | 2020-06-19 | 成都大汇物联科技有限公司 | Knowledge graph-based water mill room fault diagnosis method |
| CN111311059B (en) * | 2020-01-16 | 2023-08-29 | 成都大汇物联科技有限公司 | Waterwheel house fault diagnosis method based on knowledge graph |
| CN114297255A (en) * | 2021-12-17 | 2022-04-08 | 中电信数智科技有限公司 | Network quality work order fault early warning method based on log analysis |
| CN114297255B (en) * | 2021-12-17 | 2024-04-19 | 中电信数智科技有限公司 | Network quality work order fault early warning method based on log analysis |
| CN116810825A (en) * | 2023-08-30 | 2023-09-29 | 泓浒(苏州)半导体科技有限公司 | Wafer conveying mechanical arm abnormality monitoring method and system in vacuum environment |
| CN116810825B (en) * | 2023-08-30 | 2023-11-03 | 泓浒(苏州)半导体科技有限公司 | Wafer conveying mechanical arm abnormality monitoring method and system in vacuum environment |
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