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Fault detection method based on multi variable sequential probability ratio test of Unscented Kalman filter innovation
CAO Yu ping, TIAN Xue min
(College of Information and Control Engineering in China University of Petroleum,Dongying 257061,China)
Abstract:
A multi variable sequential probability ratio test(SPRT)method based on predictive innovation was proposed for nonlinear multi variable process monitoring problem. Firstly Unscented Kalman filter (UKF) is conducted to predict outputs using normal process model, the predictive innovation is generated by comparing predictive outputs and the actual ones which are sensed from the process. Then multi variable SPRT method is introduced to analyze the statistical characteristics of the multi dimension innovation. Decision function and decision rules with log probability likelihood ratio are constructed to monitor the status of the process and signal the faults. The simulation results on continuous stirred tank reactor show that the proposed method can monitor process effectively. Compared with the traditional weighted sum squared residual method, the proposed method has low false alarm rate and detects faults quickly.
Key words:  multi variable sequential probability ratio test  Unscented Kalman filter  fault detection  innovation