Orthogonal Stationary Component Analysis for Nonstationary Process Monitoring

Youqing Wang, Tongze Hou, Mingliang Cui, Xin Ma

Abstract: Load fluctuations, unexpected disturbances, and switching of operating states typically make actual industrial processes exhibit nonstationary. In nonstationary processes, the statistical characteristics of the data will change. It is hard to distinguish process faults and changes in statistical characteristics and hence leads to false alarms. Cointegration analysis (CA) specializes in solving difficulties caused by time-varying means and variances by finding long-term equilibrium relationships among multiple variables. However, components extracted by CA are nonorthogonal, making it difficult to detect incipient faults or having high computational costs. To provide more effective orthogonal components for nonstationary cases, a new process monitoring algorithm called orthogonal stationary component analysis (OSCA) is proposed. The proposed OSCA orthogonalizes the components and subsequently uses a stationary component selection method based on the explained variance to determine the long-run equilibrium of the variables. The performance of OSCA is verified with a closed-loop continuous stirred tank reactor (CSTR) and an actual power plant dataset.