Switching LDS-based approach for process fault detection and classification

Dynamic and uncertainty are two main features of the industrial processes data which should be paid attentions when carrying out process monitoring and fault diagnosis. As a typical dynamic Bayesian network model, linear dynamic system (LDS) can efficiently deal with both dynamic and uncertain features of the process data. This paper proposes a switching form of the LDS model for fault detection and classification. A novel and convenient learning algorithm is developed for parameter estimation of the switching LDS model, and the Gaussian Sum Filtering method is introduced for online fault classification. Besides, a switching LDS based threshold statistic is defined for unknown fault detection. Detailed comparative studies are carried out on the Tennessee Eastman (TE) benchmark process among Fisher Discriminant Analysis (FDA), Support Vector Machines (SVM), Hidden Markov Model (HMM), and the proposed method. Simulation results show the superiority of switching LDS over other three methods in terms of fault classification. Furthermore, switching LDS model provides an additional online fault classification mechanism. Simulation results in the same process demonstrate that switching LDS approach can achieve accurate detection rate of the unknown fault with minimal false alarms.