Large-scale processes under closed-loop control are commonly subjected to frequently varying conditions due to load changes or other causes, resulting in typical nonstationary characteristics. For closed-loop control processes, the normal changes in operation conditions may distort the static and dynamic variations in a different way from real faults. In this paper, a dynamic distributed monitoring strategy is proposed to separate the dynamic variations from the steady states, and concurrently, monitor them to distinguish changes in the normal operating condition and real faults for large-scale nonstationary processes under closed-loop control. First, large-scale nonstationary process variables are decomposed into different blocks to mine the local information. Second, the static and dynamic equilibrium relations are separated by probing into the cointegration analysis solution in each block. Third, the concurrent monitoring models are constructed to supervise both the steady variations and their dynamic counterparts for each block. Finally, the local monitoring results are combined by Bayesian inference to obtain global results, which enable description and monitoring of both static and dynamic equilibrium relations from the global and local viewpoints. The feasibility and performance of the proposed method are illustrated with a real industrial process, which is a 1000-MW ultra-supercritical thermal power unit.

中文翻译：

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闭环控制下频繁变化条件下大规模非平稳过程的动态分布式监测策略

闭环控制下的大规模过程通常由于负载变化或其他原因而受到频繁变化的条件的影响，从而导致典型的非平稳特性。对于闭环控制过程，运行条件​​的正常变化可能会以不同于实际故障的方式扭曲静态和动态变化。在本文中，提出了一种动态分布式监控策略，将动态变化与稳态分离，同时监控它们以区分闭环控制下大规模非平稳过程的正常运行条件的变化和实际故障。首先，将大规模非平稳过程变量分解为不同的块以挖掘局部信息。第二，静态和动态平衡关系是通过探索每个块中的协整分析解来分离的。第三，构建并发监控模型以监控每个区块的稳定变化及其动态变化。最后，通过贝叶斯推理将局部监测结果结合起来得到全局结果，从而能够从全局和局部的角度描述和监测静态和动态均衡关系。该方法的可行性和性能通过一个真实的工业过程来说明，这是一个 1000 兆瓦的超超临界火电机组。局部监测结果通过贝叶斯推理组合得到全局结果，从而能够从全局和局部的角度描述和监测静态和动态平衡关系。该方法的可行性和性能通过一个真实的工业过程来说明，这是一个 1000 兆瓦的超超临界火电机组。局部监测结果通过贝叶斯推理组合得到全局结果，从而能够从全局和局部的角度描述和监测静态和动态平衡关系。该方法的可行性和性能通过一个真实的工业过程来说明，这是一个 1000 兆瓦的超超临界火电机组。