A prognostic model for multi-stage degraded equipment under zero life label combining CDBN and Bayesian bidirectional GRU

Author:

Pei Hong,Si Xiaosheng,Zhang Zhengxin,Du Dangbo,Hu Changhua,Chang Xinlong

Funder

Henan Postdoctoral Science Foundation

Key Program of NSFC-Tongyong Union Foundation

Young Talent Support Program of Shaanxi Province University

Publisher

Elsevier BV

Reference32 articles.

1. Remaining useful life with self-attention assisted physics-informed neural network;Liao;Adv. Eng. Inf.,2023

2. Degradation path approximation for remaining useful life estimation;Fan;Adv. Eng. Inf.,2024

3. A hybrid method combining lévy process and neural network for predicting remaining useful life of rotating machinery;Lv;Adv. Eng. Inform.,2024

4. A review on physics-informed data-driven remaining useful life prediction: Challenges and opportunities;Li;Mech. Syst. Sig. Process.,2024

5. Interactive prognosis framework between deep learning and a stochastic process model for remaining useful life prediction;Pei;IEEE Trans. Neural Networks Learn. Syst.,2023

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