Prediction of bearing remaining useful life based on an improved GRU

Xun Yang; Chuanbo Wen

doi:10.1117/12.3053064

3 January 2025 Prediction of bearing remaining useful life based on an improved GRU

Xun Yang, Chuanbo Wen

Author Affiliations +

Proceedings Volume 13442, Fifth International Conference on Signal Processing and Computer Science (SPCS 2024); 1344228 (2025) https://doi.org/10.1117/12.3053064
Event: Fifth International Conference on Signal Processing and Computer Science (SPCS 2024), 2024, Kaifeng, China

Abstract

To address the challenge of enhancing the accuracy of Remaining Useful Life (RUL) predictions for bearings with deep learning models, we propose a novel method based on an improved Gated Recurrent Unit (GRU). First, Adaptive Noise-Assisted Complete Ensemble Empirical Mode Decomposition (CEEMDAN) method is utilized to extract essential degradation features from the signals. These features are then integrated using a GRU enhanced with a temporal attention mechanism. The final step involves predicting the RUL using a particle filter algorithm. Tests on the PHM2012 dataset validate that this method significantly improves the accuracy of RUL predictions.

(2025) Published by SPIE. Downloading of the abstract is permitted for personal use only.

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Xun Yang and Chuanbo Wen "Prediction of bearing remaining useful life based on an improved GRU", Proc. SPIE 13442, Fifth International Conference on Signal Processing and Computer Science (SPCS 2024), 1344228 (3 January 2025); https://doi.org/10.1117/12.3053064

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KEYWORDS

Feature extraction

Particle filters

Feature fusion

Particles

Erbium

Time-frequency analysis

Transformers

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