Fault detection and diagnosis based on principal component analysis

Abstract

In this chapter, PCA, relative PCA (RPCA) [37,38] and normalization PCA (NPCA) are introduced with application in fault detection and fault diagnosis. There are some theories and applications about PCA, such as the basic principles of PCA, geometrical interpretation of PCA, Hotelling's T2 statistic and SPE statistic for fault detection's control limit. Then a fault detection method based on PCA is introduced for Tennessee Eastman (TE) process. What's more, the fault diagnosis method based on PCA is introduced with its application for inverter. There are some theories and application about RPCA, such as the definition of Relative Transform, basic principles of RPCA and geometrical interpretation of RPCA. Then the fault detection method based on RPCA is introduced with its application. In addition, in order to improve the control limit of PCA with Hotelling's T2, the dynamic data window control limit algorithm based on RPCA is introduced with its application. As follows, the fault diagnosis method based on RPCA is introduced with its application. There are some theories and application about NPCA, such as the definition of longitudinal standardization (LS) and basic principles of NPCA. Next a fault detection method based on NPCA is presented with its application in wind power generation. Then another fault detection method based on NPCA is presented with its application in DC motor. In order to increase the control limit of PCA with Hotelling's T2, a fault detection method based on NPCA-adaptive confidence limit (ACL) is presented with application in DC motor.