Knowledge-informed deep networks for robust fault diagnosis of rolling bearings
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DOI: 10.1016/j.ress.2023.109863
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- Chen, Jinglong & Pan, Jun & Li, Zipeng & Zi, Yanyang & Chen, Xuefeng, 2016. "Generator bearing fault diagnosis for wind turbine via empirical wavelet transform using measured vibration signals," Renewable Energy, Elsevier, vol. 89(C), pages 80-92.
- Wang, Xu & Shen, Changqing & Xia, Min & Wang, Dong & Zhu, Jun & Zhu, Zhongkui, 2020. "Multi-scale deep intra-class transfer learning for bearing fault diagnosis," Reliability Engineering and System Safety, Elsevier, vol. 202(C).
- Xu, Zhaoyi & Saleh, Joseph Homer, 2021. "Machine learning for reliability engineering and safety applications: Review of current status and future opportunities," Reliability Engineering and System Safety, Elsevier, vol. 211(C).
- Zhou, Taotao & Zhang, Laibin & Han, Te & Droguett, Enrique Lopez & Mosleh, Ali & Chan, Felix T.S., 2023. "An uncertainty-informed framework for trustworthy fault diagnosis in safety-critical applications," Reliability Engineering and System Safety, Elsevier, vol. 229(C).
- Chen, Xuejun & Yang, Yongming & Cui, Zhixin & Shen, Jun, 2019. "Vibration fault diagnosis of wind turbines based on variational mode decomposition and energy entropy," Energy, Elsevier, vol. 174(C), pages 1100-1109.
- Duan, Chaoqun & Makis, Viliam & Deng, Chao, 2020. "A two-level Bayesian early fault detection for mechanical equipment subject to dependent failure modes," Reliability Engineering and System Safety, Elsevier, vol. 193(C).
- Xiaohan Chen & Beike Zhang & Dong Gao, 2021. "Bearing fault diagnosis base on multi-scale CNN and LSTM model," Journal of Intelligent Manufacturing, Springer, vol. 32(4), pages 971-987, April.
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Keywords
Fault diagnosis; Network optimization; Knowledge-informed deep learning; Convolutional neural network; Constrained Gaussian process;All these keywords.
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