RUL prediction of rolling bearings across working conditions based on multi-scale convolutional parallel memory domain adaptation network
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DOI: 10.1016/j.ress.2023.109854
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References listed on IDEAS
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Cited by:
- Kim, Sunghyun & Seo, Yun-Ho & Park, Junhong, 2024. "Transformer-based novel framework for remaining useful life prediction of lubricant in operational rolling bearings," Reliability Engineering and System Safety, Elsevier, vol. 251(C).
- Lin, Yan-Hui & Chang, Liang & Guan, Lu-Xin, 2024. "Enhanced stochastic recurrent hybrid model for RUL Predictions via Semi-supervised learning," Reliability Engineering and System Safety, Elsevier, vol. 248(C).
- Wang, Chen & Zhang, Liming & Chen, Ling & Tan, Tian & Zhang, Cong, 2025. "Remaining useful life prediction of nuclear reactor control rod drive mechanism based on dynamic temporal convolutional network," Reliability Engineering and System Safety, Elsevier, vol. 253(C).
- Han, Yan & Hu, Ailin & Huang, Qingqing & Zhang, Yan & Lin, Zhichao & Ma, Jinghua, 2025. "Sinkhorn divergence-based contrast domain adaptation for remaining useful life prediction of rolling bearings under multiple operating conditions," Reliability Engineering and System Safety, Elsevier, vol. 253(C).
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Keywords
Rolling bearing RUL prediction; Multi-scale convolution; Parallel memory network; Across working conditions; Domain adaptation;All these keywords.
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