PCA-SVM Based Fault Prognosis for Flue Gas Turbine

Author

Jie Ma ; Qiuyan Wang ; Aiming Dong

Author_Institution

Dept. of Autom., Beijing Inf. Sci. & Technol. Univ., Beijing, China

fYear

2012

fDate

8-10 Dec. 2012

Firstpage

1304

Lastpage

1308

Abstract

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.

Keywords

fault diagnosis; flue gases; gas turbines; maintenance engineering; power engineering computing; principal component analysis; support vector machines; PCA-SVM based fault prognosis; flue gas turbine; multivariate fault prognosis; principal component analysis; squared prediction error index; statistical process monitoring methods; time series prediction; Monitoring; Noise reduction; Predictive models; Principal component analysis; Support vector machines; Turbines; Vibrations; SVM model; fault prognosis; principal component analysis; squared prediction error; statistical process monitoring;

fLanguage

English

Publisher

ieee

Conference_Titel

Instrumentation, Measurement, Computer, Communication and Control (IMCCC), 2012 Second International Conference on

Conference_Location

Harbin

Print_ISBN

978-1-4673-5034-1

Type

conf

DOI

10.1109/IMCCC.2012.307

Filename

6429143