An enhanced encoder–decoder framework for bearing remaining useful life prediction. Liu, L., Song, X., Chen, K., Hou, B., Chai, X., & Ning, H. Measurement, November, 2020. ![An enhanced encoder–decoder framework for bearing remaining useful life prediction [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex 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.
@article{liu_enhanced_2020,
title = {An enhanced encoder–decoder framework for bearing remaining useful life prediction},
issn = {0263-2241},
url = {http://www.sciencedirect.com/science/article/pii/S0263224120312537},
doi = {10.1016/j.measurement.2020.108753},
abstract = {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.},
language = {en},
urldate = {2020-11-23},
journal = {Measurement},
author = {Liu, Lu and Song, Xiao and Chen, Kai and Hou, Baocun and Chai, Xudong and Ning, Huansheng},
month = nov,
year = {2020},
keywords = {Cumulative operation, Encoder–decoder, Fitness analysis, Trigonometric functions},
pages = {108753},
}
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