Enhanced Prediction of the Remaining Useful Life of Rolling Bearings Under Cross-Working Conditions via an Initial Degradation Detection-Enabled Joint Transfer Metric Network

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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Bibliographic Details
Main Authors: Lingfeng Qi, Jiafang Pan, Tianping Huang, Zhenfeng Zhou, Faguo Huang
Format: Article
Language:English
Published: MDPI AG 2025-06-01
Series:Applied Sciences
Subjects:
remaining useful life prediction
reconstruction along projection pathway
deep adversarial convolution autoencoder network
joint domain adaptation loss
Online Access:https://www.mdpi.com/2076-3417/15/12/6401
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https://www.mdpi.com/2076-3417/15/12/6401

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