Nonstationary Process Monitoring Based on Cointegration Theory and Multiple Order Moments

Abstract

In industrial processes, process data often exhibit complex characteristics, such as nonstationarity and nonlinearity, which brings challenges to process monitoring. In this study, a monitoring strategy for nonstationary processes is proposed based on cointegration theory and multiple order moments. Considering the nonstationarity presented in some variables, cointegration analysis (CA) is applied to obtain long-term equilibrium relationships among these nonstationary variables, which are then combined with stationary variables to form a new stationary dataset. For the purpose of process monitoring, a new monitoring index that contains multiple order moments is proposed to capture different statistical features of a previously obtained stationary data set. Moving windows are applied to capture changes of local statistical characteristics to implement online monitoring. Case studies on simulation data and an industrial dataset are presented to illustrate the effectiveness of the proposed method for nonstationary process monitoring. Comparing with the PCA and common CA-based monitoring methods, the proposed method has better performance with a lower false alarm rate and earlier alarm time.