Deep transfer learning based on dynamic domain adaptation for remaining useful life prediction under different working conditions
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DOI: 10.1007/s10845-021-01814-y
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Cited by:
- Yi Lyu & Zhenfei Wen & Aiguo Chen, 2025. "A novel transfer learning approach based on deep degradation feature adaptive alignment for remaining useful life prediction with multi-condition data," Journal of Intelligent Manufacturing, Springer, vol. 36(1), pages 619-637, January.
- Li, Jimeng & Mao, Weilin & Yang, Bixin & Meng, Zong & Tong, Kai & Yu, Shancheng, 2024. "RUL prediction of rolling bearings across working conditions based on multi-scale convolutional parallel memory domain adaptation network," Reliability Engineering and System Safety, Elsevier, vol. 243(C).
- Han, Yaoyao & Ding, Xiaoxi & Gu, Fengshou & Chen, Xiaohui & Xu, Minmin, 2025. "Dual-drive RUL prediction of gear transmission systems based on dynamic model and unsupervised domain adaption under zero sample," Reliability Engineering and System Safety, Elsevier, vol. 253(C).
- Chao Huang & Siqi Bu & Hiu Hung Lee & Kwong Wah Chan & Winco K. C. Yung, 2024. "Prognostics and health management for induction machines: a comprehensive review," Journal of Intelligent Manufacturing, Springer, vol. 35(3), pages 937-962, March.
- Jiaxian Chen & Dongpeng Li & Ruyi Huang & Zhuyun Chen & Weihua Li, 2025. "A transfer regression network-based adaptive calibration method for remaining useful life prediction considering individual discrepancies in the degradation process of machinery," Journal of Intelligent Manufacturing, Springer, vol. 36(4), pages 2767-2783, April.
- Pei Wang & Tao Wang & Sheng Yang & Han Cheng & Pengde Huang & Qianle Zhang, 2024. "Production quality prediction of cross-specification products using dynamic deep transfer learning network," Journal of Intelligent Manufacturing, Springer, vol. 35(6), pages 2567-2592, August.
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Keywords
Remaining useful life prediction; Dynamic domain adaptation; Domain invariance degradation feature; Multiple working conditions;All these keywords.
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