Deep learning-based remaining useful life estimation of bearings using multi-scale feature extraction. Li, X., Zhang, W., & Ding, Q. Reliability Engineering & System Safety, 182:208–218, February, 2019. ![Deep learning-based remaining useful life estimation of bearings using multi-scale feature extraction [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex Accurate evaluation of machine degradation during long-time operation is of great importance. With the rapid development of modern industries, physical model is becoming less capable of describing sophisticated systems, and data-driven approaches have been widely developed. This paper proposes a novel intelligent remaining useful life (RUL) prediction method based on deep learning. The time-frequency domain information is explored for prognostics, and multi-scale feature extraction is implemented using convolutional neural networks. Experiments on a popular rolling bearing dataset prepared from the PRONOSTIA platform are carried out to show the effectiveness of the proposed method, and its superiority is demonstrated by the comparisons with other approaches. In general, high accuracy on the RUL prediction is achieved, and the proposed method is promising for industrial applications.
@article{li_deep_2019,
title = {Deep learning-based remaining useful life estimation of bearings using multi-scale feature extraction},
volume = {182},
issn = {0951-8320},
url = {https://www.sciencedirect.com/science/article/pii/S0951832018308299},
doi = {10.1016/j.ress.2018.11.011},
abstract = {Accurate evaluation of machine degradation during long-time operation is of great importance. With the rapid development of modern industries, physical model is becoming less capable of describing sophisticated systems, and data-driven approaches have been widely developed. This paper proposes a novel intelligent remaining useful life (RUL) prediction method based on deep learning. The time-frequency domain information is explored for prognostics, and multi-scale feature extraction is implemented using convolutional neural networks. Experiments on a popular rolling bearing dataset prepared from the PRONOSTIA platform are carried out to show the effectiveness of the proposed method, and its superiority is demonstrated by the comparisons with other approaches. In general, high accuracy on the RUL prediction is achieved, and the proposed method is promising for industrial applications.},
language = {en},
urldate = {2021-09-28},
journal = {Reliability Engineering \& System Safety},
author = {Li, Xiang and Zhang, Wei and Ding, Qian},
month = feb,
year = {2019},
keywords = {Deep learning, Multi-scale feature extraction, Prognostics and health management, Remaining useful life, Rolling bearing},
pages = {208--218},
}
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