An end-to-end framework for remaining useful life prediction of rolling bearing based on feature pre-extraction mechanism and deep adaptive transformer model

• Published in: Computers & industrial engineering Vol. 161; p. 107531
• Main Authors: Su, Xuanyuan, Liu, Hongmei, Tao, Laifa, Lu, Chen, Suo, Mingliang
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01-11-2021
• Subjects: Adaptive transformer model; Deep learning; Feature pre-extraction mechanism; Remaining useful life prediction; Rolling bearing; Deep learning; Remaining useful life prediction; Feature pre-extraction mechanism; Rolling bearing; Adaptive transformer model
• ISSN: 0360-8352; 1879-0550
• DOI: 10.1016/j.cie.2021.107531

•Pre-extraction mechanism is put forward to perform the feature construction.•Adaptive transformer is proposed for the complete degradation modeling.•An end-to-end deep framework is proposed for RUL prediction of bearings.•Two targeted case studies are designed to illustrate our superiority.
In practical engineering, accurate prediction of remaining useful life (RUL) is always necessary for effective preparation of engineering assets, human resources and maintenance actions. With the improvement of computing power and the passionate requirements for the high prediction accuracy of complex systems, more and more deep model-based frameworks have been developed for RUL prediction. In general, these frameworks consist of two stages: the first one is the manual operation of feature extraction and feature selection; the second one is the RUL prediction that mainly employs the recurrent deep models. However, such the frameworks do not fully take advantage of the deep models since they still rely on much prior knowledge and do not achieve the satisfied prediction performance. In this paper, a novel two stage framework with less prior knowledge, namely, end-to-end framework, is proposed to improve the forecasting performance. In our first stage, a feature pre-extraction mechanism is designed to pre-extract the low-level features in relatively high dimensional space, which requires no additional manual operations of feature fusion and feature selection in existing methods. In our second stage, adaptive transformer, a new deep model integrating the attention mechanism and the recurrent architecture, is proposed to model the relationships between these low-level features and the RULs directly, which suppresses the issue of vanishing gradients and is more suitable for representing the complex temporal degradation characteristics. Two public bearing datasets are employed to validate the effectiveness of the proposed framework in this paper. In these two case studies, some existing state-of-the-art RUL prediction approaches are fully compared, and the critical hyperparameters and components of our framework are analyzed in details. The experimental results reveal our advantage on adaptive degradation modeling and accurate RUL prediction, and help to interpret the impact of the proposed framework architecture on bearing RUL prediction.