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- W3092320313 abstract "Remaining useful lifetime (RUL) estimation of engineering assets is of great importance in an efficient operation of a production system. This paper deals with the RUL estimation with deep convolutional neural networks (DCNN) of bearings being operated in different load conditions by measuring the horizontal and vertical vibration. We present the use of Sobolev training to enhance the prediction capabilities of the DCNN since deeper architectures generally perform better than their shallower counterparts. We test the proposed approach on the benchmark IEEE PHM 2012 data challenge datasets for RUL prediction and compare the results with standard DCNN approaches. The results show that the proposed training methodology consistently outperform the standard training approach for RUL estimation even though the model size and complexity was much lower." @default.
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- W3092320313 date "2020-09-01" @default.
- W3092320313 modified "2023-09-25" @default.
- W3092320313 title "Remaining Useful Lifetime Estimation with Sobolev Training" @default.
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- W3092320313 doi "https://doi.org/10.1109/etfa46521.2020.9212061" @default.
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