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• W3126429103 startingPage "109090" @default.
• W3126429103 abstract "Rolling bearing is a vital part of the machinery, whose remaining useful life (RUL) estimation plays a critical role in ensuring the safety and maintenance decision-making. However, in most industrial applications, it is difficult to obtain run-to-failure data under complex operating conditions, which is inefficient for deep learning approaches. To solve the above problem, a new approach using transfer depth-wise separable convolution recurrent network (TDSCRN) for RUL estimation of bearing is presented. A novel prediction model so-called depth-wise separable convolution recurrent network (DSCRN) is designed and trained by the source-domain dataset. The parameters and model of DSCRN are transferred to the target-domain, and then TDSCRN is obtained for RUL estimation task. Two public run-to-failure datasets are used to validate the performance of the presented method. The results indicate that this framework can improve estimation accuracy and robustness in complex operating conditions." @default.
• W3126429103 created "2021-02-15" @default.
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• W3126429103 creator A5047977577 @default.
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• W3126429103 date "2021-05-01" @default.
• W3126429103 modified "2023-10-06" @default.
• W3126429103 title "Transfer remaining useful life estimation of bearing using depth-wise separable convolution recurrent network" @default.
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• W3126429103 doi "https://doi.org/10.1016/j.measurement.2021.109090" @default.
• W3126429103 hasPublicationYear "2021" @default.
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