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• W4282942000 abstract "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." @default.
• W4282942000 created "2022-06-16" @default.
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• W4282942000 date "2023-06-01" @default.
• W4282942000 modified "2023-10-09" @default.
• W4282942000 title "Adaptive Multimode Process Monitoring Based on Mode-Matching and Similarity-Preserving Dictionary Learning" @default.
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• W4282942000 doi "https://doi.org/10.1109/tcyb.2022.3178878" @default.
• W4282942000 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/35687634" @default.
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