Probabilistic Stationary Subspace Analysis for Monitoring Nonstationary Industrial Processes With Uncertainty

Abstract

Actual industrial processes often show nonstationary characteristics, so nonstationary process monitoring is significant to ensure the safety and reliability of industrial processes. However, existing monitoring methods for nonstationary processes usually ignore process uncertainties, caused by random noises and unknown disturbances. It is worth noting that process uncertainties may degrade the monitoring performance for incipient faults, and result in over-fitting of model parameters. To address the problem of monitoring nonstationary industrial processes with uncertainty, a novel algorithm called probabilistic stationary subspace analysis (PSSA) is proposed in this article. PSSA explicitly models process uncertainties, and distinguishes actual process variations from the uncertainty. In view of the coupling between model parameters, the expectation maximization algorithm is used to estimate the parameters of PSSA, and the closed-form updates are derived in detail. Based on PSSA, two detection statistics are designed for process monitoring. Finally, the effective performance of the proposed method is demonstrated by three case studies, including a numerical example, a closed-loop continuous stirred tank reactor, and a real power plant at Zhejiang Provincial Energy Group of China.