ORIGINAL RESEARCH article
Front. Signal Process.
Sec. Systems Health Diagnosis and Prognosis
Residual Life Prediction of Progressive Failure Bearings Based on NGO-AVMD Hybrid Domain Features
Provisionally accepted
- Shanghai Shentong Metro Group, Shanghai, China
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Accurate bearing Remaining Useful Life (RUL) prediction is vital for equipment availability, cost reduction, and safety. Existing data-driven methods often yield insufficient accuracy due to single-scale feature extraction and poor differentiation of failure modes. This paper proposes a hybrid-domain feature extraction method, integrating original vibration signals with Adaptive Variational Mode Decomposition optimized by Northern Goshawk Optimization (NGO-AVMD) reconstructed signals and additional deep features. These mixed-domain features are used to compute a health index that effectively distinguishes progressive and sudden bearing failure modes. Focusing on progressive degradation, a multi-attention Temporal Convolutional Network (TCN) is then employed for RUL prediction, using these features as input. Validated on the PHM2012 dataset, the method achieves an R2 of 98%, demonstrating its high accuracy in bearing life prediction.
Keywords: Bearing RUL, health index, MA-TCN, Mixed-domain features, NGO-AVMD
Received: 22 Oct 2025; Accepted: 25 Dec 2025.
Copyright: © 2025 Jiong. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Zhou Jiong
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