PCA-AR based fault prognosis for turbine machine

Author

Wang, Qiuyan ; Ma, Jie ; Xu, Xiaoli

Author_Institution

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

fYear

2011

fDate

7-10 Aug. 2011

Firstpage

1605

Lastpage

1610

Abstract

In this paper, a multivariate fault prognosis approach based on statistical process monitoring (SPM) methods and time series prediction for turbine machine 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 AR 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 huge stack gas turbine shows the efficiency of the proposed approach.

Keywords

fault diagnosis; gas turbines; petrochemicals; principal component analysis; time series; turbines; PCA model; PCA-AR based fault prognosis; gas turbine; multivariate fault prognosis; principal component analysis; sample data; squared prediction error index; statistical process monitoring; time series prediction; turbine machine; Indexes; Monitoring; Noise reduction; Predictive models; Principal component analysis; Turbines; Vibrations; AR model; fault prognosis; principal component analysis; squared prediction error; statistical process monitoring;

fLanguage

English

Publisher

ieee

Conference_Titel

Mechatronics and Automation (ICMA), 2011 International Conference on

Conference_Location

Beijing

ISSN

2152-7431

Print_ISBN

978-1-4244-8113-2

Type

conf

DOI

10.1109/ICMA.2011.5985954

Filename

5985954