PCA-SVM based Fault Prognosis for Flue Gas Turbine

被引：3

作者：

Ma, Jie ^{[1
]}

Wang, Qiuyan ^{[1
]}

Dong, Aiming

机构：

[1] Beijing Informat Sci & Technol Univ, Dept Automat, Beijing 100192, Peoples R China

来源：

PROCEEDINGS OF THE 2012 SECOND INTERNATIONAL CONFERENCE ON INSTRUMENTATION & MEASUREMENT, COMPUTER, COMMUNICATION AND CONTROL (IMCCC 2012) | 2012年

基金：

中国国家自然科学基金; 北京市自然科学基金;

关键词：

fault prognosis; statistical process monitoring; principal component analysis; squared prediction error; SVM model;

D O I：

10.1109/IMCCC.2012.307

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

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 corres ponding 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.

引用

页码：1304 / 1308

页数：5

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