A novel deep learning method based on attention mechanism for bearing remaining useful life prediction

• Published in: Applied soft computing Vol. 86; p. 105919
• Main Authors: Chen, Yuanhang, Peng, Gaoliang, Zhu, Zhiyu, Li, Sijue
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.01.2020
• Subjects: Attention mechanism; Recurrent neural network; Remaining useful life prediction; Recurrent neural network; Remaining useful life prediction; Attention mechanism
• ISSN: 1568-4946; 1872-9681
• DOI: 10.1016/j.asoc.2019.105919

Rolling bearing is a key component in rotation machine, whose remaining useful life (RUL) prediction is an essential issue of constructing condition-based maintenance (CBM) system. However, recent data-driven approaches for bearing RUL prediction still require prior knowledge to extract features, construct health indicate (HI) and set up threshold, which is inefficient in the big data era. In this paper, a pure data-driven method for bearing RUL prediction with little prior knowledge is proposed. This method includes three steps, i.e., features extraction, HI prediction and RUL calculation. In the first step, five band-pass energy values of frequency spectrum are extracted as features. Then, a recurrent neural network based on encoder–decoder framework with attention mechanism is proposed to predict HI values, which are designed closely related with the RUL values in this paper. Finally, the final RUL value can be obtained via linear regression. Experiments carried out on the dataset from PRONOSTIA and comparison with other novel approaches demonstrate that the proposed method achieves a better performance. •A novel neural network for bearing remaining useful life prediction is proposed.•This end-to-end method with attention mechanism required little prior knowledge.•Useful degradation information can be mined from long historic data.