Wang Jianshi
Research Subjects
Wang Jianshi has a total of 4 co-authors. Here are the top co-authors by number of shared publications:
- Bei Guangxia1 shared publication
- Yang Haikun1 shared publication
- Yongchao Zhang
College of Mechanical and Electronic Engineering, Shandong University of Science and Technology, Qingdao 266590, China
1 shared publication - Zhang Shengru1 shared publication
Latest Publications
- Open Access
- 10 August 2023
- Published by MDPI AG in Preprints.org
Remaining Life Prediction of Rolling Bearings Based on Grey Wolf Optimized Network
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.
- 10 August 2023
- Published by MDPI AG in Preprints.org
Open AccessRemaining Life Prediction of Rolling Bearings Based on Grey Wolf Optimized Network
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.