Abstract Many continuous industrial processes operate in different steady states with different grades or products. The switching between two steady states is called transition. Transition consists of a series of operation changes that should be carried out in proper order, within certain magnitudes and time region. Since faulty operation may lead to increase in inferior products or even hazard events, monitoring of the transition is desired. In this work, a transition identification and monitoring scheme is proposed based on slow feature analysis. Two monitoring statistics which represent the location of the trajectory and the speed of transition are proposed. Besides, operating faults are generated based on the guidewords of hazard and operability analysis (HAZOP). Using a numerical case and the mode 4-to-2 transition of the Tennessee-Eastman process in which catastrophic failures exist, the effectiveness of the proposed method is validated. In addition to missed detection rate and false alarm rate, two performance indexes known as detection time (DT) and rescue time (RT) are introduced. The advantages of proposed method are benchmarked against the stage-based sub principle component analysis(sub-PCA) and the global preserving statistics slow feature analysis(GSSFA).

中文翻译：

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基于轨迹的多模工艺过渡过程运行监控

摘要 许多连续的工业过程在不同的稳态下运行，具有不同的等级或产品。两个稳态之间的切换称为过渡。过渡包括一系列操作变化，这些变化应该在一定的量级和时间范围内以适当的顺序进行。由于错误的操作可能会导致劣质产品甚至危险事件的增加，因此需要对过渡进行监控。在这项工作中，提出了一种基于慢特征分析的过渡识别和监测方案。提出了两个代表轨迹位置和过渡速度的监测统计数据。此外，操作故障是根据危险和可操作性分析（HAZOP）的指导语生成的。使用数值案例和存在灾难性故障的田纳西-伊士曼过程的模式 4 到 2 转换，验证了所提出方法的有效性。除了漏检率和误报率外，还引入了检测时间（DT）和救援时间（RT）两个性能指标。将所提方法的优点与基于阶段的子主成分分析（sub-PCA）和全局保留统计慢特征分析（GSSFA）进行了对比。