Multitask learning of health state assessment and remaining useful life prediction for sensor-equipped machines
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DOI: 10.1016/j.ress.2023.109141
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Cited by:
- 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).
- Zhang, Chen & Hu, Di & Yang, Tao, 2024. "Research of artificial intelligence operations for wind turbines considering anomaly detection, root cause analysis, and incremental training," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
- Zhou, Liang & Wang, Huawei, 2024. "An adaptive multi-scale feature fusion and adaptive mixture-of-experts multi-task model for industrial equipment health status assessment and remaining useful life prediction," Reliability Engineering and System Safety, Elsevier, vol. 248(C).
- 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).
- Zhang, Yadong & Zhang, Chao & Wang, Shaoping & Dui, Hongyan & Chen, Rentong, 2024. "Health indicators for remaining useful life prediction of complex systems based on long short-term memory network and improved particle filter," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
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Keywords
Prognostics and health management; Health state assessment; Remaining useful life prediction; Multitask deep learning model; Attention mechanism; Prediction intervals;All these keywords.
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