Multiple Component Analysis and Its Application in Process Monitoring With Prior Fault Data

Abstract

Traditional principal component analysis (PCA) based process monitoring method is unsupervised learning method, which builds a statistical model only based on the normal operating dataset. However, in industrial process database, there are also some faulty operating datasets available which are omitted by PCA but may be helpful for fault detection performance improvement. To better utilize both normal operating data and prior faulty data, a modified PCA method, called multiple component analysis (MCA) is presented for monitoring process faults. MCA statistical modelling involves two kinds of data feature extractions. Firstly, based on normal operating data, MCA applies conventional PCA transformation to obtain the principal component features, which describe normal data distribution directions. Then, for the known fault data, non-local preservation projection technique is used to compute fault discriminant features, which describe the fault data distribution directions. Lastly these features are integrated to construct monitoring statistics for fault detection. Simulations on Tennessee Eastman benchmark process show that the proposed method outperforms traditional PCA method in terms of fault detection performance.