Fault Detection for Closed-Loop Control Systems Based on Parity Space Transformation

Abstract

Fault detection for closed-loop control systems is the future development in the field of the fault diagnosis. Since a closed-loop control system is generally very robust to the external disturbances, fault detection has been challenging a hot research area. Traditional data-driven detection methods are not particularly designed for closed-loop control systems and thus can be improved. In this paper, a new fault detection method is proposed, which is based on the parity space for the closed-loop control system. The main principle of our method is to transform the detection residual into the parity space of the original space to restrict false detection or leak detection caused by the estimation of uncertain states. More specifically, the construction of the stable kernel matrix in the parity space is given, and the residual sequence is accumulated to improve the fault-to-noise ratio and thus increase the detection performance. To verify our method, we have conducted a simulation which is based on a numerical simulation model and the Tennessee industrial system respectively. The results show that the proposed method is more feasible and more effective in fault detection for closed-loop control systems compared with the traditional data-driven detection methods, including the time series modeling method and the partial least squares method.