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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Vydáno v:Applied sciences Ročník 15; číslo 12; s. 6401
Hlavní autoři: Qi, Lingfeng, Pan, Jiafang, Huang, Tianping, Zhou, Zhenfeng, Huang, Faguo
Médium: Journal Article
Jazyk:angličtina
Vydáno: Basel MDPI AG 01.06.2025
Témata:
Adaptation
Artificial intelligence
Bearings
deep adversarial convolution autoencoder network
Deep learning
Failure
joint domain adaptation loss
Liu, Timothy
Machine learning
Methods
Neural networks
reconstruction along projection pathway
remaining useful life prediction
Useful life
Work environment
Workers
Working conditions
ISSN:2076-3417, 2076-3417
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Shrnutí: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.
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ISSN:2076-3417
2076-3417
DOI:10.3390/app15126401