A Fault Diagnosis Approach for Gas Turbine Exhaust Gas Temperature Based on Fuzzy C-Means Clustering and Support Vector Machine
As an important gas path performance parameter of gas turbine, exhaust gas temperature (EGT) can represent the thermal health condition of gas turbine. In order to monitor and diagnose the EGT effectively, a fusion approach based on fuzzy C-means (FCM) clustering algorithm and support vector machine...
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2015-01-01
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Series: | Mathematical Problems in Engineering |
Online Access: | http://dx.doi.org/10.1155/2015/240267 |
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doaj-0365bfed99524808954630ce4c6c072a2020-11-24T21:17:04ZengHindawi LimitedMathematical Problems in Engineering1024-123X1563-51472015-01-01201510.1155/2015/240267240267A Fault Diagnosis Approach for Gas Turbine Exhaust Gas Temperature Based on Fuzzy C-Means Clustering and Support Vector MachineZhi-tao Wang0Ning-bo Zhao1Wei-ying Wang2Rui Tang3Shu-ying Li4College of Power and Energy Engineering, Harbin Engineering University, Harbin 150001, ChinaCollege of Power and Energy Engineering, Harbin Engineering University, Harbin 150001, ChinaCollege of Power and Energy Engineering, Harbin Engineering University, Harbin 150001, ChinaHarbin Marine Boiler & Turbine Research Institute, Harbin 150078, ChinaCollege of Power and Energy Engineering, Harbin Engineering University, Harbin 150001, ChinaAs an important gas path performance parameter of gas turbine, exhaust gas temperature (EGT) can represent the thermal health condition of gas turbine. In order to monitor and diagnose the EGT effectively, a fusion approach based on fuzzy C-means (FCM) clustering algorithm and support vector machine (SVM) classification model is proposed in this paper. Considering the distribution characteristics of gas turbine EGT, FCM clustering algorithm is used to realize clustering analysis and obtain the state pattern, on the basis of which the preclassification of EGT is completed. Then, SVM multiclassification model is designed to carry out the state pattern recognition and fault diagnosis. As an example, the historical monitoring data of EGT from an industrial gas turbine is analyzed and used to verify the performance of the fusion fault diagnosis approach presented in this paper. The results show that this approach can make full use of the unsupervised feature extraction ability of FCM clustering algorithm and the sample classification generalization properties of SVM multiclassification model, which offers an effective way to realize the online condition recognition and fault diagnosis of gas turbine EGT.http://dx.doi.org/10.1155/2015/240267 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Zhi-tao Wang Ning-bo Zhao Wei-ying Wang Rui Tang Shu-ying Li |
spellingShingle |
Zhi-tao Wang Ning-bo Zhao Wei-ying Wang Rui Tang Shu-ying Li A Fault Diagnosis Approach for Gas Turbine Exhaust Gas Temperature Based on Fuzzy C-Means Clustering and Support Vector Machine Mathematical Problems in Engineering |
author_facet |
Zhi-tao Wang Ning-bo Zhao Wei-ying Wang Rui Tang Shu-ying Li |
author_sort |
Zhi-tao Wang |
title |
A Fault Diagnosis Approach for Gas Turbine Exhaust Gas Temperature Based on Fuzzy C-Means Clustering and Support Vector Machine |
title_short |
A Fault Diagnosis Approach for Gas Turbine Exhaust Gas Temperature Based on Fuzzy C-Means Clustering and Support Vector Machine |
title_full |
A Fault Diagnosis Approach for Gas Turbine Exhaust Gas Temperature Based on Fuzzy C-Means Clustering and Support Vector Machine |
title_fullStr |
A Fault Diagnosis Approach for Gas Turbine Exhaust Gas Temperature Based on Fuzzy C-Means Clustering and Support Vector Machine |
title_full_unstemmed |
A Fault Diagnosis Approach for Gas Turbine Exhaust Gas Temperature Based on Fuzzy C-Means Clustering and Support Vector Machine |
title_sort |
fault diagnosis approach for gas turbine exhaust gas temperature based on fuzzy c-means clustering and support vector machine |
publisher |
Hindawi Limited |
series |
Mathematical Problems in Engineering |
issn |
1024-123X 1563-5147 |
publishDate |
2015-01-01 |
description |
As an important gas path performance parameter of gas turbine, exhaust gas temperature (EGT) can represent the thermal health condition of gas turbine. In order to monitor and diagnose the EGT effectively, a fusion approach based on fuzzy C-means (FCM) clustering algorithm and support vector machine (SVM) classification model is proposed in this paper. Considering the distribution characteristics of gas turbine EGT, FCM clustering algorithm is used to realize clustering analysis and obtain the state pattern, on the basis of which the preclassification of EGT is completed. Then, SVM multiclassification model is designed to carry out the state pattern recognition and fault diagnosis. As an example, the historical monitoring data of EGT from an industrial gas turbine is analyzed and used to verify the performance of the fusion fault diagnosis approach presented in this paper. The results show that this approach can make full use of the unsupervised feature extraction ability of FCM clustering algorithm and the sample classification generalization properties of SVM multiclassification model, which offers an effective way to realize the online condition recognition and fault diagnosis of gas turbine EGT. |
url |
http://dx.doi.org/10.1155/2015/240267 |
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