Towards trustworthy machine fault diagnosis: A probabilistic Bayesian deep learning framework

Citations

Cited by:

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6. Zhang, Xingwu & Zhao, Yu & Yu, Xiaolei & Ma, Rui & Wang, Chenxi & Chen, Xuefeng, 2023. "Weighted domain separation based open set fault diagnosis," Reliability Engineering and System Safety, Elsevier, vol. 239(C).
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9. Yue, Ke & Li, Jipu & Deng, Shuhan & Kwoh, Chee Keong & Chen, Zhuyun & Li, Weihua, 2024. "A relationship-aware calibrated prototypical network for fault incremental diagnosis of electric motors without reserved samples," Reliability Engineering and System Safety, Elsevier, vol. 252(C).
10. Zhou, Chengyu & Fang, Xiaolei, 2023. "A convex two-dimensional variable selection method for the root-cause diagnostics of product defects," Reliability Engineering and System Safety, Elsevier, vol. 229(C).
11. Wang, Jian & Gao, Shibin & Yu, Long & Liu, Xingyang & Neri, Ferrante & Zhang, Dongkai & Kou, Lei, 2024. "Uncertainty-aware trustworthy weather-driven failure risk predictor for overhead contact lines," Reliability Engineering and System Safety, Elsevier, vol. 242(C).
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14. Wang, Jian & Gao, Shibin & Yu, Long & Zhang, Dongkai & Xie, Chenlin & Chen, Ke & Kou, Lei, 2023. "Data-driven lightning-related failure risk prediction of overhead contact lines based on Bayesian network with spatiotemporal fragility model," Reliability Engineering and System Safety, Elsevier, vol. 231(C).
15. Xu, Jinjin & Wang, Rongxi & Liang, Zeming & Liu, Pengpeng & Gao, Jianmin & Wang, Zhen, 2023. "Physics-guided, data-refined fault root cause tracing framework for complex electromechanical system," Reliability Engineering and System Safety, Elsevier, vol. 236(C).
16. Zhu, Zuanyu & Cheng, Junsheng & Wang, Ping & Wang, Jian & Kang, Xin & Yang, Yu, 2023. "A novel fault diagnosis framework for rotating machinery with hierarchical multiscale symbolic diversity entropy and robust twin hyperdisk-based tensor machine," Reliability Engineering and System Safety, Elsevier, vol. 231(C).
17. Wu, Hao & Xu, Yanwen & Liu, Zheng & Li, Yumeng & Wang, Pingfeng, 2023. "Adaptive machine learning with physics-based simulations for mean time to failure prediction of engineering systems," Reliability Engineering and System Safety, Elsevier, vol. 240(C).
18. Dai, Menghang & Liu, Zhiliang & Wang, Jinrui & Zuo, Mingjian, 2024. "Physics-driven feature alignment combined with dynamic distribution adaptation for three-cylinder drilling pump cross-speed fault diagnosis," Reliability Engineering and System Safety, Elsevier, vol. 251(C).
19. Rathnakumar, Rahul & Pang, Yutian & Liu, Yongming, 2023. "Epistemic and aleatoric uncertainty quantification for crack detection using a Bayesian Boundary Aware Convolutional Network," Reliability Engineering and System Safety, Elsevier, vol. 240(C).
20. Zhang, Chao & Gong, Daqing & Xue, Gang, 2025. "An uncertainty-incorporated active data diffusion learning framework for few-shot equipment RUL prediction," Reliability Engineering and System Safety, Elsevier, vol. 254(PB).
21. Wang, Jian & Liu, Huiyuan & Gao, Shibin & Yu, Long & Liu, Xingyang & Zhang, Dongkai & Kou, Lei, 2024. "Robust deep Gaussian process-based trustworthy fog-haze-caused pollution flashover prediction approach for overhead contact lines," Reliability Engineering and System Safety, Elsevier, vol. 243(C).
22. Li, Hao & Jiao, Jinyang & Liu, Zongyang & Lin, Jing & Zhang, Tian & Liu, Hanyang, 2025. "Trustworthy Bayesian deep learning framework for uncertainty quantification and confidence calibration: Application in machinery fault diagnosis," Reliability Engineering and System Safety, Elsevier, vol. 255(C).
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26. 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).
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29. Pan, Junlin & Sun, Bo & Wu, Zeyu & Yi, Zechen & Feng, Qiang & Ren, Yi & Wang, Zili, 2024. "Probabilistic remaining useful life prediction without lifetime labels: A Bayesian deep learning and stochastic process fusion method," Reliability Engineering and System Safety, Elsevier, vol. 250(C).
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