A Distributional Perspective on Remaining Useful Life Prediction with Deep Learning and Quantile Regression

Abstract

With the rapid development of information and sensor technology, the data-driven remaining useful lifetime (RUL) prediction methods have been acquired a successful development. Nowadays, the data-driven RUL methods are focused on estimating the RUL value. However, it is more important to quantify uncertainty associated with the RUL value. This is because increasingly complex industrial systems would arise various sources of uncertainty. This paper proposes a novel distributional RUL prediction method, which aims at quantifying the RUL uncertainty by identifying the confidence interval with the cumulative distribution function (CDF). The proposed learning method has been built based on quantile regression and implemented from a distributional perspective under the deep neural network framework. The results of the run-to-failure degradation experiments of rolling bearing demonstrate the effectiveness and good performance of the proposed method compared to other state-of-the-art methods. The visualization results obtained by t-SNE technology have been investigated to further verify the effectiveness and generalization ability of the proposed method.

Publication DOI: https://doi.org/10.1109/OJIM.2022.3205649
Divisions: College of Engineering & Physical Sciences > School of Engineering and Technology > Mechanical, Biomedical & Design
College of Engineering & Physical Sciences
Additional Information: This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/ Funding Information: European Commission Horizon 2020 research and innovation programme (Grant Number: 869884)
Uncontrolled Keywords: Distributional RUL prediction,Deep learning,Quantile Regression,Uncertainty,Rolling Bearing
Last Modified: 27 May 2024 07:39
Date Deposited: 14 Sep 2022 13:36
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Related URLs: https://ieeexpl ... authors#authors (Publisher URL)
PURE Output Type: Article
Published Date: 2022-09-29
Published Online Date: 2022-09-12
Accepted Date: 2022-08-26
Authors: Zhang, Ming (ORCID Profile 0000-0001-5202-5574)
Wang, Duo
Amaitik, Nasser (ORCID Profile 0000-0002-0962-4341)
Xu, Yuchun (ORCID Profile 0000-0001-6388-813X)

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