Fault detection and identification method based on multivariate statistical techniques

Multivariate statistical methods such as principal component analysis (PCA) and partial least squares (PLS) have been widely applied to the statistical process monitoring and their effectiveness for fault detection is well recognized, but they have a drawback: the fault diagnosis. In this paper a new method to detect and diagnosis faults is proposed that is composed of two parts: first the PLS method is used for detecting faults and the Fisher's discriminant analysis (FDA) is used for diagnosing the faults. FDA provides an optimal lower dimensional representation in terms of discriminating between classes of data, where, in this context of fault diagnosis, each class corresponds to data collected during a specific, known fault. A real plant is used to demonstrate the performance of the proposed method.