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首页> 外文期刊>Mathematical Problems in Engineering >A Fault Diagnosis Approach for Gas Turbine Exhaust Gas Temperature Based on Fuzzy C-Means Clustering and Support Vector Machine
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A Fault Diagnosis Approach for Gas Turbine Exhaust Gas Temperature Based on Fuzzy C-Means Clustering and Support Vector Machine

机译：基于模糊C均值聚类和支持向量机的燃气轮机排气温度故障诊断方法。

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摘要

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.
机译：排气温度（EGT）作为燃气轮机重要的燃气路径性能参数，可以代表燃气轮机的热健康状况。为了有效地对EGT进行监测和诊断，提出了一种基于模糊C均值（FCM）聚类算法和支持向量机（SVM）分类模型的融合方法。考虑到燃气轮机EGT的分布特性，采用FCM聚类算法进行聚类分析并获得状态模式，在此基础上完成对EGT的预分类。然后，设计了支持向量机的多分类模型，以进行状态模式识别和故障诊断。例如，分析了来自工业燃气轮机的EGT的历史监测数据，并用于验证本文提出的融合故障诊断方法的性能。结果表明，该方法可以充分利用FCM聚类算法的无监督特征提取能力和SVM多分类模型的样本分类泛化特性，为实现燃气轮机EGT在线状态识别和故障诊断提供了有效的途径。

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  • 来源
    《Mathematical Problems in Engineering》 |2015年第10期|240267.1.-240267.11|共11页
  • 作者

    Wang Zhi-tao; Zhao Ning-bo; Wang Wei-ying; Tang Rui; Li Shu-ying;

  • 作者单位

    Harbin Engn Univ, Coll Power & Energy Engn, Harbin 150001, Peoples R China.;

    Harbin Engn Univ, Coll Power & Energy Engn, Harbin 150001, Peoples R China.;

    Harbin Marine Boiler & Turbine Res Inst, Harbin 150078, Peoples R China.;

    Harbin Marine Boiler & Turbine Res Inst, Harbin 150078, Peoples R China.;

    Harbin Engn Univ, Coll Power & Energy Engn, Harbin 150001, Peoples R China.;

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