Prediction of remaining useful life based on bidirectional gated recurrent unit with temporal self-attention mechanism. (May 2022)

• Record Type: Journal Article
• Authors: Zhang, Jiusi; Jiang, Yuchen; Wu, Shimeng; Li, Xiang; Luo, Hao; Yin, Shen
• Is Part Of: Reliability engineering & system safety. Volume 221(2022)
• Publication Date: 2022-05
• Subjects: Prognostics health management -- Remaining useful life -- Temporal self-attention mechanism -- Bidirectional gated recurrent unit -- Prediction; Reliability (Engineering) -- Periodicals; System safety -- Periodicals; Industrial safety -- Periodicals; Fiabilité -- Périodiques; Sécurité des systèmes -- Périodiques; Sécurité du travail -- Périodiques; 620.00452
• DOI: 10.1016/j.ress.2021.108297
• Languages: English
• ISSNs: 0951-8320

Abstract

Abstract: 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. Highlights: 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.