RESEARCH PAPER
A Frequency-Adaptive Feature Extraction Framework for Bearing Remaining Useful Life Prediction
1
Sino-European Institute of Aviation Engineering, Civil Aviation University of China, China
2
College of Electronic Information and Automation, Civil Aviation University of China, China
Submission date: 2025-11-20
Final revision date: 2026-01-26
Acceptance date: 2026-02-13
Online publication date: 2026-02-24
Corresponding author
Runxia Guo
College of Electronic Information and Automation, Civil Aviation University of China, China
College of Electronic Information and Automation, Civil Aviation University of China, China
KEYWORDS
TOPICS
ABSTRACT
Accurate prediction of bearing remaining useful life (RUL) is essential for reliable rotating machinery. However, multi-sensor degradation signals exhibit diverse temporal and spectral patterns that are often insufficiently captured by feature extractors using uniform processing, limiting their ability to model signal-specific degradation behavior and affecting prediction accuracy. This study proposes a frequency-adaptive feature extraction framework for bearing RUL prediction. The framework includes a Temporal Feature Extraction Network (TFEN) that employs dilated convolutions with adaptive configurations to capture degradation dynamics across multiple temporal scales, and a Transformer-based Spatial Feature Extraction Network (SFEN) to model inter-sensor dependencies. By aligning feature extraction with the dominant frequency characteristics of each sensor channel, the proposed method improves the representation of degradation features. Experiments on two bearing datasets demonstrate its effectiveness, showing consistently enhanced prediction accuracy relative to existing models.
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| eISSN: | 2956-3860 |
| ISSN: | 1507-2711 |
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