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Remaining Useful Life Prediction Across Conditions Based on a Health Indicator-Weighted Subdomain Alignment Network.

Zhiqing Xu¹, Christopher W K Chow¹, Md Mizanur Rahman¹

¹Sustainable Infrastructure and Resource Management (SIRM), UniSA STEM, University of South Australia, Mawson Lakes, SA 5095, Australia.

Sensors (Basel, Switzerland)

|August 14, 2025

Summary

This summary is machine-generated.

This study introduces a new Health Indicator-Weighted Subdomain Alignment Network (HIWSAN) for predicting bearing remaining useful life (RUL). HIWSAN improves accuracy by analyzing multi-scale features and temporal weights, outperforming existing methods.

Keywords:

bearing prognostics contrastive learning health indicator remaining useful life prediction subdomain adaptation

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Area of Science:

Mechanical Engineering
Artificial Intelligence
Machine Learning

Background:

Domain adaptation (DA) is crucial for predicting bearing remaining useful life (RUL) across different operating conditions.
Existing DA methods often neglect multi-scale degradation information and temporal dynamics, limiting prediction accuracy.
Accurate RUL prediction is vital for predictive maintenance and operational efficiency.

Purpose of the Study:

To propose a novel domain adaptation model, the Health Indicator-Weighted Subdomain Alignment Network (HIWSAN), for enhanced bearing RUL prediction.
To address limitations in existing methods by incorporating multi-scale feature learning, health indicators as temporal weights, and subdomain-level alignment.
To evaluate the effectiveness and generalization capabilities of HIWSAN on benchmark datasets.

Main Methods:

Developed the Health Indicator-Weighted Subdomain Alignment Network (HIWSAN) model.
Implemented multi-scale feature representation learning.
Constructed health indicators to serve as temporal weights for model training.
Performed subdomain-level alignment to bridge domain gaps.

Main Results:

HIWSAN achieved an average Mean Absolute Error (MAE) of 0.0989 and Root Mean Square Error (RMSE) of 0.1189 across the XJTU-SY and PRONOSTIA datasets.
The proposed model demonstrated significant improvements, with MAE reduced by 21.07% and RMSE by 25.13% compared to state-of-the-art methods.
Ablation, comparison, and generalization experiments validated the model's performance and robustness.

Conclusions:

HIWSAN effectively extracts multi-scale degradation information and utilizes temporal weights for accurate bearing RUL prediction.
The subdomain-level alignment strategy enhances domain adaptation performance in RUL prediction tasks.
The proposed model offers a promising advancement for predictive maintenance in rotating machinery.