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• W3049631036 abstract "Fault monitoring can find out-of-control conditions of equipment operation in a timely manner, which is essential for eliminating faults and for stable operation of industrial systems in batch processes. Many conventional data-driven fault detection methods focus less on the non-Gaussian and Multi-stage characteristics of batch process data, which may result in degradation of monitoring performance. In this paper, a Multi-stage Fourth Order Moment Staked Autoencoder (M-FOM-SAE) is designed to solve the above problems. The proposed method firstly automatically determines the number of clusters and divides the batch process into multiple stages. After that, the FOM-SAE model is established in each sub-stage, which can not only effectively learn the nonlinear features of process data, but also extract the non-Gaussian information. The proposed strategy is applied to real-world industrial processes. Experimental results indicate that it can better capture the non-Gaussian and Multi-stage characteristics of process data, and improve the ability to monitor abnormalities." @default.
• W3049631036 created "2020-08-21" @default.
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• W3049631036 date "2020-08-01" @default.
• W3049631036 modified "2023-10-16" @default.
• W3049631036 title "Batch Process Monitoring Based on Multi-stage Fourth Order Moment Stacked Autoencoder" @default.
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• W3049631036 doi "https://doi.org/10.1109/ccdc49329.2020.9164590" @default.
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