A Deep Learning-Based Framework for Bearing RUL Prediction to Optimize Laser Shock Peening Remanufacturing

被引:0
|
作者
Liang, Yuchen [1 ]
Wang, Yuqi [2 ]
Li, Anping [1 ]
Gu, Chengyi [3 ]
Tang, Jie [3 ]
Pang, Xianjuan [4 ]
机构
[1] Jiangsu Univ, Sch Mech Engn, Zhenjiang 212013, Peoples R China
[2] Wuhan Univ Technol, Sch Transportat & Logist Engn, Wuhan 430070, Peoples R China
[3] Jiangsu Haiyu Machinery Co Ltd, Taizhou 225714, Jiangsu, Peoples R China
[4] Henan Univ Sci & Technol, Natl United Engn Lab Adv Bearing Tribol, Luoyang 471000, Peoples R China
来源
APPLIED SCIENCES-BASEL | 2024年 / 14卷 / 22期
基金
中国博士后科学基金;
关键词
bearing RUL prediction; laser shock peening; deep learning; remanufacturing; data pre-processing;
D O I
10.3390/app142210493
中图分类号
O6 [化学];
学科分类号
0703 ;
摘要
Accurate prediction of the remaining useful life (RUL) of bearings is crucial for maintaining the reliability and efficiency of industrial systems. This study introduces a novel methodology integrating advanced machine learning and optimization techniques to address this challenge. (1) A transformer-attention model was developed to process segmented vibration signals, effectively capturing complex patterns. The model showed better performance than traditional approaches, with an RMSE of 0.989. (2) A Deep Neural Network (DNN) was designed to predict the extended RUL of bearings after laser shock peening (LSP) remanufacturing. The fruit fly optimization (FFO) algorithm was employed to optimize the remanufacturing parameters; a 29.33% improvement was achieved in fitness compared to the baseline. (3) The DNN model predictions were validated against Finite Element Analysis (FEA) simulations, with a low relative error of 2.5% to 5.8%; the model showed good accuracy in capturing the effects of optimized LSP parameters on bearing life extension.
引用
收藏
页数:16
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