A reliable technique for remaining useful life estimation of rolling element bearings using dynamic regression models

Citations

Cited by:

1. 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).
2. Miao, Mengqi & Yu, Jianbo & Zhao, Zhihong, 2022. "A sparse domain adaption network for remaining useful life prediction of rolling bearings under different working conditions," Reliability Engineering and System Safety, Elsevier, vol. 219(C).
3. Wei, Jianfeng & Zhang, Faping & Lu, Jiping, 2025. "Health indicator construction based on Double attribute feature deviation degree and its application into RUL prediction," Reliability Engineering and System Safety, Elsevier, vol. 256(C).
4. Guishuang Tian & Shaoping Wang & Jian Shi & Yajing Qiao, 2022. "State Estimation and Remaining Useful Life Prediction of PMSTM Based on a Combination of SIR and HSMM," Sustainability, MDPI, vol. 14(24), pages 1-21, December.
5. Liu, Shujie & Fan, Lexian, 2022. "An adaptive prediction approach for rolling bearing remaining useful life based on multistage model with three-source variability," Reliability Engineering and System Safety, Elsevier, vol. 218(PB).
6. Bahareh Tajiani & Jørn Vatn, 2023. "Adaptive remaining useful life prediction framework with stochastic failure threshold for experimental bearings with different lifetimes under contaminated condition," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 14(5), pages 1756-1777, October.
7. Ma, Chenyang & Li, Yongbo & Wang, Xianzhi & Cai, Zhiqiang, 2023. "Early fault diagnosis of rotating machinery based on composite zoom permutation entropy," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
8. Zeki Murat Çınar & Abubakar Abdussalam Nuhu & Qasim Zeeshan & Orhan Korhan & Mohammed Asmael & Babak Safaei, 2020. "Machine Learning in Predictive Maintenance towards Sustainable Smart Manufacturing in Industry 4.0," Sustainability, MDPI, vol. 12(19), pages 1-42, October.
9. Wang, Han & Wang, Dongdong & Liu, Haoxiang & Tang, Gang, 2022. "A predictive sliding local outlier correction method with adaptive state change rate determining for bearing remaining useful life estimation," Reliability Engineering and System Safety, Elsevier, vol. 225(C).
10. Xiang, Sheng & Qin, Yi & Luo, Jun & Pu, Huayan & Tang, Baoping, 2021. "Multicellular LSTM-based deep learning model for aero-engine remaining useful life prediction," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
11. Kumar, Anil & Kumar, Rajesh & Tang, Hesheng & Xiang, Jiawei, 2024. "A comprehensive study on developing an intelligent framework for identification and quantitative evaluation of the bearing defect size," Reliability Engineering and System Safety, Elsevier, vol. 242(C).
12. Mariusz Zieja & Andrzej Gębura & Andrzej Szelmanowski & Bartłomiej Główczyk, 2021. "Non-Invasive Monitoring of the Technical Condition of Power Units Using the FAM-C and FDM-A Electrical Methods," Sustainability, MDPI, vol. 13(23), pages 1-19, December.
13. Qiao, Yajing & Wang, Shaoping & Shi, Jian & Liu, Di & Tao, Mo, 2024. "Reliability model based on fault energy dissipation for mechatronic system," Reliability Engineering and System Safety, Elsevier, vol. 250(C).
14. Wu, Jingyao & Zhao, Zhibin & Sun, Chuang & Yan, Ruqiang & Chen, Xuefeng, 2021. "Learning from Class-imbalanced Data with a Model-Agnostic Framework for Machine Intelligent Diagnosis," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
15. Wang, Zhijie & Zhai, Qingqing & Chen, Piao, 2021. "Degradation modeling considering unit-to-unit heterogeneity-A general model and comparative study," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
16. Li, Guofa & Wei, Jingfeng & He, Jialong & Yang, Haiji & Meng, Fanning, 2023. "Implicit Kalman filtering method for remaining useful life prediction of rolling bearing with adaptive detection of degradation stage transition point," Reliability Engineering and System Safety, Elsevier, vol. 235(C).
17. Cheng, Han & Kong, Xianguang & Wang, Qibin & Ma, Hongbo & Yang, Shengkang, 2022. "The two-stage RUL prediction across operation conditions using deep transfer learning and insufficient degradation data," Reliability Engineering and System Safety, Elsevier, vol. 225(C).
18. 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.
19. Saeed, Umer & Jan, Sana Ullah & Lee, Young-Doo & Koo, Insoo, 2021. "Fault diagnosis based on extremely randomized trees in wireless sensor networks," Reliability Engineering and System Safety, Elsevier, vol. 205(C).
20. Han Cheng & Xianguang Kong & Qibin Wang & Hongbo Ma & Shengkang Yang & Gaige Chen, 2023. "Deep transfer learning based on dynamic domain adaptation for remaining useful life prediction under different working conditions," Journal of Intelligent Manufacturing, Springer, vol. 34(2), pages 587-613, February.
21. Lee, Jinwook & Kim, Myungyon & Ko, Jin Uk & Jung, Joon Ha & Sun, Kyung Ho & Youn, Byeng D., 2022. "Asymmetric inter-intra domain alignments (AIIDA) method for intelligent fault diagnosis of rotating machinery," Reliability Engineering and System Safety, Elsevier, vol. 218(PB).
22. 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).
23. Han, Xiao & Wang, Zili & Xie, Min & He, Yihai & Li, Yao & Wang, Wenzhuo, 2021. "Remaining useful life prediction and predictive maintenance strategies for multi-state manufacturing systems considering functional dependence," Reliability Engineering and System Safety, Elsevier, vol. 210(C).
24. Liu, Junqiang & Yu, Zhuoqian & Zuo, Hongfu & Fu, Rongchunxue & Feng, Xiaonan, 2022. "Multi-stage residual life prediction of aero-engine based on real-time clustering and combined prediction model," Reliability Engineering and System Safety, Elsevier, vol. 225(C).
25. González-Muñiz, Ana & Díaz, Ignacio & Cuadrado, Abel A. & García-Pérez, Diego, 2022. "Health indicator for machine condition monitoring built in the latent space of a deep autoencoder," Reliability Engineering and System Safety, Elsevier, vol. 224(C).
26. Liu, Shaoyang & Wei, Jingfeng & Li, Guofa & He, Jialong & Zhang, Baodong & Liu, Bo, 2025. "A two-stage remaining useful life prediction method based on adaptive feature metric and graph spatiotemporal attention rule learning," Reliability Engineering and System Safety, Elsevier, vol. 257(PA).
27. Bermeo-Ayerbe, Miguel Angel & Cocquempot, Vincent & Ocampo-Martinez, Carlos & Diaz-Rozo, Javier, 2023. "Remaining useful life estimation of ball-bearings based on motor current signature analysis," Reliability Engineering and System Safety, Elsevier, vol. 235(C).