Prediction of remaining useful life based on bidirectional gated recurrent unit with temporal self-attention mechanism

Prediction of remaining useful life (RUL) is of vital significance in the prognostics health management (PHM) tasks. To deal with the reverse time series and to reflect the difference in RUL prediction results at different time instances, this paper proposes a novel bidirectional gated recurrent uni...

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Vydáno v:Reliability engineering & system safety Ročník 221; s. 108297
Hlavní autoři: Zhang, Jiusi, Jiang, Yuchen, Wu, Shimeng, Li, Xiang, Luo, Hao, Yin, Shen
Médium: Journal Article
Jazyk:angličtina
Vydáno: Barking Elsevier Ltd 01.05.2022
Elsevier BV
Témata:
Bidirectional gated recurrent unit
Datasets
Deep learning
Machine learning
Milling (machining)
Prediction
Predictions
Process parameters
Prognostics health management
Reliability engineering
Remaining useful life
Temporal self-attention mechanism
Turbofan engines
Useful life
Prognostics health management
Remaining useful life
Bidirectional gated recurrent unit
Temporal self-attention mechanism
Prediction
ISSN:0951-8320, 1879-0836
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Shrnutí:Prediction of remaining useful life (RUL) is of vital significance in the prognostics health management (PHM) tasks. To deal with the reverse time series and to reflect the difference in RUL prediction results at different time instances, this paper proposes a novel bidirectional gated recurrent unit with temporal self-attention mechanism (BiGRU-TSAM) to predict RUL. Specifically, a novel approach is proposed where each of the considered time instance is assigned a self-learned weight according to the degree of significance. Furthermore, the parameter update process of the TSAM is obtained with solid theoretical foundation, and as a sign of interpretability, it is shown that the assigned weights can remain consistency over several independent training processes. On this basis, the BiGRU-TSAM is applied to predict RUL online. An aircraft turbofan engine dataset and a milling dataset are applied to verify the proposed RUL prediction approach. The experimental results show the superiority of the proposed approach over the existing ones based on machine learning and deep learning. •It is proposed to predict RUL with the aid of a novel BiGRU-TSAM network.•Each of the considered time instance is assigned a self-learned weight.•The parameter update process of the TSAM layer is obtained.•The assigned weights can remain consistency over several independent training processes.
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ISSN:0951-8320
1879-0836
DOI:10.1016/j.ress.2021.108297