Exponential Stationary Subspace Analysis for Stationary Feature Analytics and Adaptive Nonstationary Process Monitoring

For real industrial processes, time-varying behaviors are quite common. Consequently, industrial processes usually possess nonstationary characteristics, which makes conventional monitoring methods suffer from the model mismatch problem. In this article, an exponential analytic stationary subspace analysis (EASSA) algorithm is proposed to develop an adaptive strategy for nonstationary process monitoring. It is recognized that although covered by nonstationary trends, some underlying components of the process may remain stationary, which can be used for reliable process monitoring. For this, an EASSA algorithm is first developed to estimate the stationary sources more accurately and numerically stably. Then, a monitoring strategy is developed on the estimated stationary sources to provide reliable monitoring results. Considering that the relationships between process variables are driven to change slowly by time-varying behaviors, an update strategy and an adaptive monitoring scheme are designed to accurately track the trajectory of nonstationary processes while reducing the update frequency as much as possible. To meet the need of the EASSA algorithm, the minimum update unit has been expanded from one sample to one batch and the update conditions are given, by which the model is prevented from erroneously adapting to incipient faults to a certain extent. Case study on both a simulation process and a real thermal power plant process demonstrates that the proposed method can distinguish the real faults from normal changes while being robust to the disturbances in the nonstationary process.