Aiming Dong

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Qiuyan Wang

Tianjin Polytechnic University

Jie Ma

Beijing Information Science & Technology University

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Tianjin Polytechnic University

Hunan Institute of Technology

University of Shanghai for Science and Technology

Tianjin University of Technology and Education

Institute of Animal Science

First Affiliated Hospital of Soochow University

Horological Research Institute of Light Industry

Weifang People's Hospital

Soochow University

Putian University

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PCA-SVM Based Fault Prognosis for Flue Gas Turbine

Jie Ma Qiuyan Wang Aiming Dong

In this paper, a multivariate fault prognosis approach based on statistical process monitoring (SPM) methods and time series prediction for flue gas turbine was proposed. A principal component analysis (PCA) model using sample data under normal state was built. Firstly, fault is detected by squared prediction error (SPE) index, then predicted by SVM model. With development of fault process, the SPE will produce a corresponding change and carry important fault information, so calculate statistics of SPE can be characterized and predict the trend of fault and level. A case study on the flue gas turbine shows the efficiency of the proposed approach.

10.1109/imccc.2012.307

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