Deep residual LSTM with domain-invariance for remaining useful life prediction across domains

Citations

Cited by:

1. Yan, Jianhai & He, Zhen & He, Shuguang, 2023. "Multitask learning of health state assessment and remaining useful life prediction for sensor-equipped machines," Reliability Engineering and System Safety, Elsevier, vol. 234(C).
2. Xu, Danyang & Qiu, Haobo & Gao, Liang & Yang, Zan & Wang, Dapeng, 2022. "A novel dual-stream self-attention neural network for remaining useful life estimation of mechanical systems," Reliability Engineering and System Safety, Elsevier, vol. 222(C).
3. Zhang, Zhiyao & Chen, Xiaohui & Zio, Enrico & Li, Longxiao, 2023. "Multi-task learning boosted predictions of the remaining useful life of aero-engines under scenarios of working-condition shift," Reliability Engineering and System Safety, Elsevier, vol. 237(C).
4. 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).
5. 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).
6. 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).
7. Pan, Tongyang & Chen, Jinglong & Ye, Zhisheng & Li, Aimin, 2022. "A multi-head attention network with adaptive meta-transfer learning for RUL prediction of rocket engines," Reliability Engineering and System Safety, Elsevier, vol. 225(C).
8. Le Xuan, Quy & Munderloh, Marco & Ostermann, Jörn, 2024. "Self-supervised domain adaptation for machinery remaining useful life prediction," Reliability Engineering and System Safety, Elsevier, vol. 250(C).
9. Ding, Peng & Zhao, Xiaoli & Shao, Haidong & Jia, Minping, 2023. "Machinery cross domain degradation prognostics considering compound domain shifts," Reliability Engineering and System Safety, Elsevier, vol. 239(C).
10. Lin Lin & Jie Liu & Feng Guo & Changsheng Tong & Lizheng Zu & Hao Guo, 2022. "ERDERP: Entity and Relation Double Embedding on Relation Hyperplanes and Relation Projection Hyperplanes," Mathematics, MDPI, vol. 10(22), pages 1-19, November.
11. Dong, Yutong & Jiang, Hongkai & Wu, Zhenghong & Yang, Qiao & Liu, Yunpeng, 2023. "Digital twin-assisted multiscale residual-self-attention feature fusion network for hypersonic flight vehicle fault diagnosis," Reliability Engineering and System Safety, Elsevier, vol. 235(C).
12. Zhang, Jiusi & Tian, Jilun & Yan, Pengfei & Wu, Shimeng & Luo, Hao & Yin, Shen, 2024. "Multi-hop graph pooling adversarial network for cross-domain remaining useful life prediction: A distributed federated learning perspective," Reliability Engineering and System Safety, Elsevier, vol. 244(C).
13. Ren, Xiangyu & Qin, Yong & Li, Bin & Wang, Biao & Yi, Xiaojian & Jia, Limin, 2024. "A core space gradient projection-based continual learning framework for remaining useful life prediction of machinery under variable operating conditions," Reliability Engineering and System Safety, Elsevier, vol. 252(C).
14. Hyunsoo Kim & Jiseok Jeong & Changwan Kim, 2022. "Daily Peak-Electricity-Demand Forecasting Based on Residual Long Short-Term Network," Mathematics, MDPI, vol. 10(23), pages 1-17, November.
15. Song, Wanqing & Duan, Shouwu & Zio, Enrico & Kudreyko, Aleksey, 2022. "Multifractional and long-range dependent characteristics for remaining useful life prediction of cracking gas compressor," Reliability Engineering and System Safety, Elsevier, vol. 225(C).
16. Wang, Chu & Dou, Manfeng & Li, Zhongliang & Outbib, Rachid & Zhao, Dongdong & Zuo, Jian & Wang, Yuanlin & Liang, Bin & Wang, Peng, 2023. "Data-driven prognostics based on time-frequency analysis and symbolic recurrent neural network for fuel cells under dynamic load," Reliability Engineering and System Safety, Elsevier, vol. 233(C).
17. Chen, Dingliang & Qin, Yi & Qian, Quan & Wang, Yi & Liu, Fuqiang, 2023. "Transfer life prediction of gears by cross-domain health indicator construction and multi-hierarchical long-term memory augmented network," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
18. Kong, Ziqian & Jin, Xiaohang & Xu, Zhengguo & Chen, Zian, 2023. "A contrastive learning framework enhanced by unlabeled samples for remaining useful life prediction," Reliability Engineering and System Safety, Elsevier, vol. 234(C).
19. Liu, Yang & Zhou, Guangda & Zhao, Shujian & Li, Liang & Xie, Wenhua & Su, Bengan & Li, Yongwei & Zhao, Zhen, 2025. "A novel two-stage method via adversarial strategy for remaining useful life prediction of bearings under variable conditions," Reliability Engineering and System Safety, Elsevier, vol. 254(PA).
20. Ding, Yifei & Jia, Minping & Zhuang, Jichao & Cao, Yudong & Zhao, Xiaoli & Lee, Chi-Guhn, 2023. "Deep imbalanced domain adaptation for transfer learning fault diagnosis of bearings under multiple working conditions," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
21. Fu, Song & Lin, Lin & Wang, Yue & Guo, Feng & Zhao, Minghang & Zhong, Baihong & Zhong, Shisheng, 2024. "MCA-DTCN: A novel dual-task temporal convolutional network with multi-channel attention for first prediction time detection and remaining useful life prediction," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
22. Yan, Jianhai & Ye, Zhi-Sheng & He, Shuguang & He, Zhen, 2024. "A feature disentanglement and unsupervised domain adaptation of remaining useful life prediction for sensor-equipped machines," Reliability Engineering and System Safety, Elsevier, vol. 242(C).
23. Wang, Fujin & Zhao, Zhibin & Zhai, Zhi & Guo, Yanjie & Xi, Huan & Wang, Shibin & Chen, Xuefeng, 2023. "Feature disentanglement and tendency retainment with domain adaptation for Lithium-ion battery capacity estimation," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
24. Xiong, Jiawei & Zhou, Jian & Ma, Yizhong & Zhang, Fengxia & Lin, Chenglong, 2023. "Adaptive deep learning-based remaining useful life prediction framework for systems with multiple failure patterns," Reliability Engineering and System Safety, Elsevier, vol. 235(C).
25. Yi Lyu & Qichen Zhang & Zhenfei Wen & Aiguo Chen, 2022. "Remaining Useful Life Prediction Based on Multi-Representation Domain Adaptation," Mathematics, MDPI, vol. 10(24), pages 1-18, December.
26. Yang, Jing & Wang, Xiaomin, 2024. "Meta-learning with deep flow kernel network for few shot cross-domain remaining useful life prediction," Reliability Engineering and System Safety, Elsevier, vol. 244(C).