Robust multi-mode probabilistic slow feature analysis with application to fault detection

Abstract

This paper proposes a robust multi-mode dynamic data-driven model to identify complex industrial processes and study its application in detecting incipient faults. To model the process dynamics, a robust multi-mode probabilistic slow feature analysis (RMPSFA) is developed. A multiple switching conditional random field (MSCRF) is utilized to handle the multi-modality in the process. In order to make the model robust to the presence of outliers, a Laplace distribution is utilized to describe noise characteristics. To apply it in fault detection, several informative indices are defined. Then, the practical applicability of the proposed method is studied through an experiment on a hybrid tank pilot plant setup. Moreover, the real-life performance of the proposed model is tested on an industrial example.