Tensor representation-based transferability analytics and selective transfer learning of prognostic knowledge for remaining useful life prediction across machines
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DOI: 10.1016/j.ress.2023.109695
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References listed on IDEAS
- Zio, Enrico, 2022. "Prognostics and Health Management (PHM): Where are we and where do we (need to) go in theory and practice," Reliability Engineering and System Safety, Elsevier, vol. 218(PA).
- Zhuang, Jichao & Jia, Minping & Zhao, Xiaoli, 2022. "An adversarial transfer network with supervised metric for remaining useful life prediction of rolling bearing under multiple working conditions," Reliability Engineering and System Safety, Elsevier, vol. 225(C).
- Zhao, Ke & Hu, Junchen & Shao, Haidong & Hu, Jiabei, 2023. "Federated multi-source domain adversarial adaptation framework for machinery fault diagnosis with data privacy," Reliability Engineering and System Safety, Elsevier, vol. 236(C).
- Hu, Tao & Guo, Yiming & Gu, Liudong & Zhou, Yifan & Zhang, Zhisheng & Zhou, Zhiting, 2022. "Remaining useful life estimation of bearings under different working conditions via Wasserstein distance-based weighted domain adaptation," Reliability Engineering and System Safety, Elsevier, vol. 224(C).
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Keywords
Remaining useful life prediction; Transfer learning; Transferability analytics; LSTM; Tensor decomposition;All these keywords.
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