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• W2969372261 abstract "In the recent years, data-driven machinery fault diagnostic methods have been successfully developed, and the tasks where the training and testing data are from the same distribution have been well addressed. However, due to sensor malfunctions, the training and testing data can be collected at different places of machines, resulting in the feature space with significant distribution discrepancy. This challenging issue has received less attention in the current literature, and the existing approaches generally fail in such scenarios. This article proposes a domain adaptation method for machinery fault diagnostics based on deep learning. Adversarial training is introduced for marginal domain fusion, and unsupervised parallel data are explored to achieve conditional distribution alignments with respect to different machine health conditions. Experiments on two rotating machinery datasets are carried out for validations. The results suggest the proposed method is promising to address the fault diagnostic tasks with data from different places of machines, further enhancing applicability of data-driven methods in real industries." @default.
• W2969372261 created "2019-08-29" @default.
• W2969372261 creator A5015815806 @default.
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• W2969372261 date "2020-08-01" @default.
• W2969372261 modified "2023-10-16" @default.
• W2969372261 title "Deep Learning-Based Machinery Fault Diagnostics With Domain Adaptation Across Sensors at Different Places" @default.
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• W2969372261 doi "https://doi.org/10.1109/tie.2019.2935987" @default.
• W2969372261 hasPublicationYear "2020" @default.
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