Life-cycle modeling driven by coupling competition degradation for remaining useful life prediction
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DOI: 10.1016/j.ress.2023.109480
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Cited by:
- Zhu, Qixiang & Zhou, Zheng & Li, Yasong & Yan, Ruqiang, 2024. "Contrastive BiLSTM-enabled Health Representation Learning for Remaining Useful Life Prediction," Reliability Engineering and System Safety, Elsevier, vol. 249(C).
- Wu, Bin & Zhang, Xiaohong & Shi, Hui & Zeng, Jianchao, 2024. "Failure mode division and remaining useful life prognostics of multi-indicator systems with multi-fault," Reliability Engineering and System Safety, Elsevier, vol. 244(C).
- Zhou, Zhihao & Zhang, Wei & Yao, Peng & Long, Zhenhua & Bai, Mingling & Liu, Jinfu & Yu, Daren, 2024. "More realistic degradation trend prediction for gas turbine based on factor analysis and multiple penalty mechanism loss function," Reliability Engineering and System Safety, Elsevier, vol. 247(C).
- Cheng, Kanru & Zhang, Kunyu & Wang, Yuzhang & Yang, Chaoran & Li, Jiao & Wang, Yueheng, 2024. "Research on gas turbine health assessment method based on physical prior knowledge and spatial-temporal graph neural network," Applied Energy, Elsevier, vol. 367(C).
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Keywords
Remaining useful life (RUL) estimation; Coupling competition degradation mechanism (CCDM); Deep Markov model (DMM); Life-cycle modeling; Degradation indictor (DI);All these keywords.
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