Version 1
: Received: 27 July 2023 / Approved: 9 August 2023 / Online: 10 August 2023 (10:49:04 CEST)
How to cite:
Guangxia, B.; Shengru, Z.; Haikun, Y.; Yongchao, Z.; Jianshi, W. Remaining Life Prediction of Rolling Bearings Based on Grey Wolf Optimized Network. Preprints2023, 2023080836. https://doi.org/10.20944/preprints202308.0836.v1
Guangxia, B.; Shengru, Z.; Haikun, Y.; Yongchao, Z.; Jianshi, W. Remaining Life Prediction of Rolling Bearings Based on Grey Wolf Optimized Network. Preprints 2023, 2023080836. https://doi.org/10.20944/preprints202308.0836.v1
Guangxia, B.; Shengru, Z.; Haikun, Y.; Yongchao, Z.; Jianshi, W. Remaining Life Prediction of Rolling Bearings Based on Grey Wolf Optimized Network. Preprints2023, 2023080836. https://doi.org/10.20944/preprints202308.0836.v1
APA Style
Guangxia, B., Shengru, Z., Haikun, Y., Yongchao, Z., & Jianshi, W. (2023). Remaining Life Prediction of Rolling Bearings Based on Grey Wolf Optimized Network. Preprints. https://doi.org/10.20944/preprints202308.0836.v1
Chicago/Turabian Style
Guangxia, B., Zhang Yongchao and Wang Jianshi. 2023 "Remaining Life Prediction of Rolling Bearings Based on Grey Wolf Optimized Network" Preprints. https://doi.org/10.20944/preprints202308.0836.v1
Abstract
In order to assess the degradation state of rolling bearings and accurately grasp the remaining life information of rolling bearings, a bearing remaining life prediction method based on the grey wolf optimization algorithm to improve the BP neural network model is proposed. The method consists of two steps, firstly, using the craggy value and root mean square value to determine the first failure time of rolling bearings so as to approximate the input features, and secondly, using the Grey Wolf optimization algorithm to optimize the BP neural network to construct the degradation model of the bearings through machine learning. The method reduces the prediction error compared with conventional techniques, indicating that the method can effectively simulate the bearing degradation process and predict the remaining useful life (RUL) of the bearing.
Keywords
rolling bearings；remaining useful life；grey wolf optimization algorithm；neural network
Subject
Engineering, Mechanical Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
