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For large-scale industrial processes under closed-loop control, processdynamics directly resulting from control action are typical characteristics andmay show different behaviors between real faults and normal changes ofoperating conditions. However, conventional distributed monitoring approachesdo not consider the closed-loop control mechanism and only explore staticcharacteristics, which thus are incapable of distinguishing between realprocess faults and nominal changes of operating conditions, leading tounnecessary alarms. In this regard, this paper proposes a distributedmonitoring method for closed-loop industrial processes by concurrentlyexploring static and dynamic characteristics. First, the large-scaleclosed-loop process is decomposed into several subsystems by developing asparse slow feature analysis (SSFA) algorithm which capture changes of bothstatic and dynamic information. Second, distributed models are developed toseparately capture static and dynamic characteristics from the local and globalaspects. Based on the distributed monitoring system, a two-level monitoringstrategy is proposed to check different influences on process characteristicsresulting from changes of the operating conditions and control action, and thusthe two changes can be well distinguished from each other. Case studies areconducted based on both benchmark data and real industrial process data toillustrate the effectiveness of the proposed method.

Distributed dynamic modeling and monitoring for large-scale industrial processes under closed-loop control