In real industrial processes, factors, such as the change in manufacturing strategy and production technology lead to the creation of multimode industrial processes and the continuous emergence of new modes. Although the industrial SCADA system has accumulated a large amount of historical data, which can be used for modeling and monitoring multimode processes to a certain extent, it is difficult for the model learned from historical data to adapt to emerging modes, resulting in the model mismatch. On the other hand, updating the model with data from new modes allows the model to continuously match the new modes, but it may cause the model to lose the ability to represent the historical modes, resulting in “catastrophic forgetting.” To address these problems, this article proposed a jointly mode-matching and similarity-preserving dictionary learning (JMSDL) method, which updated the model by learning the data of new modes, so that the model can adaptively match the newly emerged modes. At the same time, a similarity metric was put forward to guarantee the representation ability of the proposed method for historical data. A numerical simulation experiment, the CSTH process experiment, and an industrial roasting process experiment indicated that the proposed JMSDL method can match new modes while maintaining its performance on the historical modes accurately. In addition, the proposed method significantly outperforms the state-of-the-art methods in terms of fault detection and false alarm rate.

中文翻译：

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基于模式匹配和相似性保持字典学习的自适应多模式过程监控


在现实的工业过程中，制造策略、生产技术的变化等因素导致多模式工业过程的产生和新模式的不断出现。虽然工业SCADA系统积累了大量的历史数据，在一定程度上可以用于多模式过程的建模和监控，但从历史数据中学习到的模型很难适应新兴模式，导致模型失配。另一方面，用新模式的数据更新模型可以让模型不断匹配新模式，但可能会导致模型失去表示历史模式的能力，导致“灾难性遗忘”。针对这些问题，本文提出了一种联合模式匹配和相似性保留字典学习（JMSDL）方法，通过学习新模式的数据来更新模型，使模型能够自适应地匹配新出现的模式。同时，提出了相似性度量来保证该方法对历史数据的表示能力。数值模拟实验、CSTH过程实验和工业焙烧过程实验表明，所提出的JMSDL方法可以匹配新模式，同时准确地保持其在历史模式上的性能。此外，所提出的方法在故障检测和误报率方面显着优于最先进的方法。