Fault monitoring-oriented transition process identification of complex industrial processes with neighbor inconsistent pair-based attribute reduction

Complex industrial processes (CIPs) tend to run in multiple operating modes due to a variety of factors, such as the changes in load or raw material quality, random interferences from external environments, and the changing of set-points due to diverse personalized demands on product qualities. Successive stable operation modes will inevitably go through a transition stage that is extremely similar to abnormal conditions (faults). Traditional fault detection methods struggle to distinguish transition processes from faults, usually leading to high false alarm rates and resulting in frequent system fluctuations with low product quality or high production consumption. Therefore, this article proposes a well-performing CIP fault monitoring scheme with intelligent identification of transition processes. Firstly, a neighbor inconsistent pairs-based incremental attribute (or process variable) reduction approach is proposed. It extracts key process variables, which can effectively reflect the intrinsic dynamical characteristics of CIPs, from process data with high dimension, complexity, inconsistency and redundancy. Successively, an adaptive optimal sliding-window-based transition process identification approach is proposed, and the corresponding sub-phase-based fault monitoring criteria are established to realize the online fault identification of CIPs. Extensive confirmatory and comparative experiments on a numerical simulation system and the benchmark Tennessee Eastman process show that the proposed method can accurately identify the transition process, which effectively improves the fault monitoring performance of CIPs.