An enhanced encoder–decoder framework for bearing remaining useful life prediction

In recent years, data-driven approaches for remaining useful life (RUL) prognostics have aroused widespread concern. Bearings act as the fundamental component of machinery and their conditioning status is closely associated with the normal operation of equipment. Hence, it is crucial to accurately p...

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Vydáno v:Measurement : journal of the International Measurement Confederation Ročník 170; s. 108753
Hlavní autoři: Liu, Lu, Song, Xiao, Chen, Kai, Hou, Baocun, Chai, Xudong, Ning, Huansheng
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier Ltd 01.01.2021
Témata:
Cumulative operation
Encoder–decoder
Fitness analysis
Trigonometric functions
Encoder–decoder
Cumulative operation
Trigonometric functions
Fitness analysis
ISSN:0263-2241, 1873-412X
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Shrnutí:In recent years, data-driven approaches for remaining useful life (RUL) prognostics have aroused widespread concern. Bearings act as the fundamental component of machinery and their conditioning status is closely associated with the normal operation of equipment. Hence, it is crucial to accurately predict the remaining useful life of bearings. This paper explores the degradation process of bearings and proposes an enhanced encoder–decoder framework. The framework attempts to construct a decoder with the ability to look back and selectively mine underlying information in the encoder. Additionally, trigonometric functions and cumulative operation are employed to enhance the quality of health indicators. To verify the effectiveness of the proposed method, vibration data from PRONOSTIA platform are utilized for RUL prognostics. Compared with several state-of-the-art methods, the experimental results demonstrate the superiority and feasibility of the proposed method. •Trigonometric functions help to improve prediction accuracy.•Cumulative transformation enhances the monotonicity and trendability.•Encoder–decoder with attention mechanism mines historical information.•The proposed method has been generalized on public platform.
ISSN:0263-2241
1873-412X
DOI:10.1016/j.measurement.2020.108753