Enhanced Prediction of the Remaining Useful Life of Rolling Bearings Under Cross-Working Conditions via an Initial Degradation Detection-Enabled Joint Transfer Metric Network
Remaining useful life (RUL) prediction of rolling bearings is of significance for improving the reliability and durability of rotating machinery. Aiming at the problem of suboptimal RUL prediction precision under cross-working conditions due to distribution discrepancies between training and testing...
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MDPI AG
2025-06-01
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| Online Access: | https://www.mdpi.com/2076-3417/15/12/6401 |
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| author | Lingfeng Qi Jiafang Pan Tianping Huang Zhenfeng Zhou Faguo Huang |
| author_facet | Lingfeng Qi Jiafang Pan Tianping Huang Zhenfeng Zhou Faguo Huang |
| author_sort | Lingfeng Qi |
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| description | Remaining useful life (RUL) prediction of rolling bearings is of significance for improving the reliability and durability of rotating machinery. Aiming at the problem of suboptimal RUL prediction precision under cross-working conditions due to distribution discrepancies between training and testing data, enhanced cross-working condition RUL prediction for rolling bearings via an initial degradation detection-enabled joint transfer metric network is proposed. Specifically, the health indicator, called reconstruction along projection pathway (RAPP), is calculated for initial degradation detection (IDD), in which RAPP is obtained from a novel deep adversarial convolution autoencoder network (DACAEN) and compares discrepancies between the input and the reconstruction by DACAEN, not only in the input space, but also in the hidden spaces, and then RUL prediction is triggered after IDD via RAPP. After that, a joint transfer metric network is proposed for cross-working condition RUL prediction. Joint domain adaptation loss, which combines representation subspace distance and variance discrepancy representation, is designed to act on the final layer of the mapping regression network to decrease data distribution discrepancies and ultimately obtain cross-domain invariant features. The experimental results from the PHM2012 dataset show that the proposed method has higher prediction accuracy and better generalization ability than typical and advanced transfer RUL prediction methods under cross-working conditions, with improvements of 0.047, 0.053, and 0.058 in the MSE, RMSE, and Score. |
| format | Article |
| id | doaj-art-e5b80ed94f004b17a3ca4adee50fd0fc |
| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-e5b80ed94f004b17a3ca4adee50fd0fc2025-08-20T03:32:27ZengMDPI AGApplied Sciences2076-34172025-06-011512640110.3390/app15126401Enhanced Prediction of the Remaining Useful Life of Rolling Bearings Under Cross-Working Conditions via an Initial Degradation Detection-Enabled Joint Transfer Metric NetworkLingfeng Qi0Jiafang Pan1Tianping Huang2Zhenfeng Zhou3Faguo Huang4Key Laboratory of Advanced Manufactuaring and Automation Technology (Guilin University of Technology), Education Department of Guangxi Zhuang Autonomous Region, Guilin 541006, ChinaKey Laboratory of Advanced Manufactuaring and Automation Technology (Guilin University of Technology), Education Department of Guangxi Zhuang Autonomous Region, Guilin 541006, ChinaKey Laboratory of Advanced Manufactuaring and Automation Technology (Guilin University of Technology), Education Department of Guangxi Zhuang Autonomous Region, Guilin 541006, ChinaKey Laboratory of Advanced Manufactuaring and Automation Technology (Guilin University of Technology), Education Department of Guangxi Zhuang Autonomous Region, Guilin 541006, ChinaKey Laboratory of Advanced Manufactuaring and Automation Technology (Guilin University of Technology), Education Department of Guangxi Zhuang Autonomous Region, Guilin 541006, ChinaRemaining useful life (RUL) prediction of rolling bearings is of significance for improving the reliability and durability of rotating machinery. Aiming at the problem of suboptimal RUL prediction precision under cross-working conditions due to distribution discrepancies between training and testing data, enhanced cross-working condition RUL prediction for rolling bearings via an initial degradation detection-enabled joint transfer metric network is proposed. Specifically, the health indicator, called reconstruction along projection pathway (RAPP), is calculated for initial degradation detection (IDD), in which RAPP is obtained from a novel deep adversarial convolution autoencoder network (DACAEN) and compares discrepancies between the input and the reconstruction by DACAEN, not only in the input space, but also in the hidden spaces, and then RUL prediction is triggered after IDD via RAPP. After that, a joint transfer metric network is proposed for cross-working condition RUL prediction. Joint domain adaptation loss, which combines representation subspace distance and variance discrepancy representation, is designed to act on the final layer of the mapping regression network to decrease data distribution discrepancies and ultimately obtain cross-domain invariant features. The experimental results from the PHM2012 dataset show that the proposed method has higher prediction accuracy and better generalization ability than typical and advanced transfer RUL prediction methods under cross-working conditions, with improvements of 0.047, 0.053, and 0.058 in the MSE, RMSE, and Score.https://www.mdpi.com/2076-3417/15/12/6401remaining useful life predictionreconstruction along projection pathwaydeep adversarial convolution autoencoder networkjoint domain adaptation loss |
| spellingShingle | Lingfeng Qi Jiafang Pan Tianping Huang Zhenfeng Zhou Faguo Huang Enhanced Prediction of the Remaining Useful Life of Rolling Bearings Under Cross-Working Conditions via an Initial Degradation Detection-Enabled Joint Transfer Metric Network Applied Sciences remaining useful life prediction reconstruction along projection pathway deep adversarial convolution autoencoder network joint domain adaptation loss |
| title | Enhanced Prediction of the Remaining Useful Life of Rolling Bearings Under Cross-Working Conditions via an Initial Degradation Detection-Enabled Joint Transfer Metric Network |
| title_full | Enhanced Prediction of the Remaining Useful Life of Rolling Bearings Under Cross-Working Conditions via an Initial Degradation Detection-Enabled Joint Transfer Metric Network |
| title_fullStr | Enhanced Prediction of the Remaining Useful Life of Rolling Bearings Under Cross-Working Conditions via an Initial Degradation Detection-Enabled Joint Transfer Metric Network |
| title_full_unstemmed | Enhanced Prediction of the Remaining Useful Life of Rolling Bearings Under Cross-Working Conditions via an Initial Degradation Detection-Enabled Joint Transfer Metric Network |
| title_short | Enhanced Prediction of the Remaining Useful Life of Rolling Bearings Under Cross-Working Conditions via an Initial Degradation Detection-Enabled Joint Transfer Metric Network |
| title_sort | enhanced prediction of the remaining useful life of rolling bearings under cross working conditions via an initial degradation detection enabled joint transfer metric network |
| topic | remaining useful life prediction reconstruction along projection pathway deep adversarial convolution autoencoder network joint domain adaptation loss |
| url | https://www.mdpi.com/2076-3417/15/12/6401 |
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