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- W3043820234 abstract "Determining whether a fault occurs locally or globally is highly important for large-scale industrial processes involving multiple operating units. Moreover, the complex non-linearity among process variables is a prominent feature of modern industries. This paper proposes a distributed-ensemble stacked autoencoder (DE-SAE) model based on deep learning technology for monitoring non-linear, large-scale, multi-unit processes. First, the deep features of the variables involved in each operating unit are extracted with the stacked autoencoder (SAE) to represent the essential structure of the unit. Two statistics are separately constructed using the deep features and the reconstruction error for detecting the faults in local units. Subsequently, the deep representations of the variables from each operating unit are modeled with the SAE to extract the global information for global monitoring. The proposed DE-SAE model uses deep learning techniques to solve the complex non-linear relationships in industrial processes, while considering their local and global information. Therefore, the method can explain the monitoring results better. Experimental results obtained from the numerical simulation and Tennessee-Eastman process confirm the feasibility and superiority of this method." @default.
- W3043820234 created "2020-07-23" @default.
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- W3043820234 date "2021-01-01" @default.
- W3043820234 modified "2023-10-07" @default.
- W3043820234 title "Distributed-ensemble stacked autoencoder model for non-linear process monitoring" @default.
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- W3043820234 doi "https://doi.org/10.1016/j.ins.2020.06.062" @default.
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