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• W4295074176 abstract "In order to realize rapid fault detection and early warning, a fault detection method based on normal operation data is proposed. Firstly, the fault detection model is constructed based on the improved deep neural network of the auto-encoder. Secondly, the unsupervised pretraining and supervised fine-tuning of the network are finished through the operation data in a normal state to solve the contradiction between the small fault sample and the large training sample required by the deep network model. The adaptive threshold of reconstruction error is used as the evaluation index of the fault state to reduce the influence of environmental factors. Experimental results show that the proposed method can detect faults effectively." @default.
• W4295074176 created "2022-09-10" @default.
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• W4295074176 date "2022-09-09" @default.
• W4295074176 modified "2023-09-26" @default.
• W4295074176 title "Fault Early Warning Based on Improved Deep Neural Network of Auto-Encoder" @default.
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• W4295074176 doi "https://doi.org/10.1155/2022/5767642" @default.
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