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• W2968421710 abstract "Abstract This paper proposes a novel fault detection and classification method via deep residual convolutional neural network (DRCNN). The DRCNN captures the deep process features represented by convolutional layers from local to global. Unlike traditional methods, this feature representation can extract the deep fault information and learn the latent fault patterns. Besides, a data preprocessing approach is also proposed to transform the shape of original data into the shape available for convolutional neural network. Finally, experiments based on the data set of Tennessee Eastman process (TEP), a chemical industrial process benchmark, show that the proposed method achieves superior fault detection and better classification performance compared with the state‐of‐the‐art methods." @default.
• W2968421710 created "2019-08-22" @default.
• W2968421710 creator A5037832358 @default.
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• W2968421710 date "2019-08-16" @default.
• W2968421710 modified "2023-10-13" @default.
• W2968421710 title "Fault detection and classification with feature representation based on deep residual convolutional neural network" @default.
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• W2968421710 doi "https://doi.org/10.1002/cem.3170" @default.
• W2968421710 hasPublicationYear "2019" @default.
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