New mode cold start monitoring in industrial processes: A solution of spatial–temporal feature transfer

Abstract

In actual industrial processes, the working conditions often change, resulting in frequent mode switching. Thus, there are no sufficient samples in the start-up stage of a new mode to build an effective model for anomaly monitoring. Meanwhile, the undesirable delay in collecting more modeling samples has posed a threat for real-time process monitoring. We propose a spatial–temporal feature transfer method to address the new mode cold start monitoring by designing a transfer linear dynamic system (TLDS). TLDS enables us to establish a satisfying monitoring model without requiring many samples from the target mode. Unlike most transfer learning methods, our method features a new domain adaptation strategy that simultaneously transfers the temporal and spatial correlations between the source and target domains instead of aligning the static correlations between the two domains. Thus, it is especially well-suited for the dynamic process industry. Moreover, we use the Kullback–Leibler (KL) divergence to align the state transition and observation generation distributions in two domains and apply the expectation maximization (EM) algorithm to estimate the parameters and states in the TLDS model. The effectiveness of this method is verified through a numerical example and the Tennessee Eastman (TE) process benchmark.