A Deep Learning-Based Framework for Bearing RUL Prediction to Optimize Laser Shock Peening Remanufacturing
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...
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| Format: | Article |
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MDPI AG
2024-11-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/14/22/10493 |
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| author | Yuchen Liang Yuqi Wang Anping Li Chengyi Gu Jie Tang Xianjuan Pang |
| author_facet | Yuchen Liang Yuqi Wang Anping Li Chengyi Gu Jie Tang Xianjuan Pang |
| author_sort | Yuchen Liang |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-a4e48ab6883d45c5863ef0683e84197e |
| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-a4e48ab6883d45c5863ef0683e84197e2024-11-26T17:48:58ZengMDPI AGApplied Sciences2076-34172024-11-0114221049310.3390/app142210493A Deep Learning-Based Framework for Bearing RUL Prediction to Optimize Laser Shock Peening RemanufacturingYuchen Liang0Yuqi Wang1Anping Li2Chengyi Gu3Jie Tang4Xianjuan Pang5School of Mechanical Engineering, Jiangsu University, Zhenjiang 212013, ChinaSchool of Transportation and Logistics Engineering, Wuhan University of Technology, Wuhan 430070, ChinaSchool of Mechanical Engineering, Jiangsu University, Zhenjiang 212013, ChinaJiangsu Haiyu Machinery Co., Ltd., Taizhou 225714, ChinaJiangsu Haiyu Machinery Co., Ltd., Taizhou 225714, ChinaNational United Engineering Laboratory for Advanced Bearing Tribology, Henan University of Science and Technology, Luoyang 471000, ChinaAccurate 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.https://www.mdpi.com/2076-3417/14/22/10493bearing RUL predictionlaser shock peeningdeep learningremanufacturingdata pre-processing |
| spellingShingle | Yuchen Liang Yuqi Wang Anping Li Chengyi Gu Jie Tang Xianjuan Pang A Deep Learning-Based Framework for Bearing RUL Prediction to Optimize Laser Shock Peening Remanufacturing Applied Sciences bearing RUL prediction laser shock peening deep learning remanufacturing data pre-processing |
| title | A Deep Learning-Based Framework for Bearing RUL Prediction to Optimize Laser Shock Peening Remanufacturing |
| title_full | A Deep Learning-Based Framework for Bearing RUL Prediction to Optimize Laser Shock Peening Remanufacturing |
| title_fullStr | A Deep Learning-Based Framework for Bearing RUL Prediction to Optimize Laser Shock Peening Remanufacturing |
| title_full_unstemmed | A Deep Learning-Based Framework for Bearing RUL Prediction to Optimize Laser Shock Peening Remanufacturing |
| title_short | A Deep Learning-Based Framework for Bearing RUL Prediction to Optimize Laser Shock Peening Remanufacturing |
| title_sort | deep learning based framework for bearing rul prediction to optimize laser shock peening remanufacturing |
| topic | bearing RUL prediction laser shock peening deep learning remanufacturing data pre-processing |
| url | https://www.mdpi.com/2076-3417/14/22/10493 |
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