INTERPRETABLE DEEP LEARNING METHOD FOR PREDICTING REMAINING USEFUL LIFE OF WIND TURBINE BEARINGS

Yu Ping; Ping Mengmeng; Ma Jialin; Cao Jie

doi:10.19912/j.0254-0096.tynxb.2024-0751

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Acta Energiae Solaris Sinica ›› 2025, Vol. 46 ›› Issue (9) : 66-75. DOI: 10.19912/j.0254-0096.tynxb.2024-0751

INTERPRETABLE DEEP LEARNING METHOD FOR PREDICTING REMAINING USEFUL LIFE OF WIND TURBINE BEARINGS

Yu Ping¹, Ping Mengmeng¹, Ma Jialin¹, Cao Jie^1,2

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Abstract

This study proposes an interpretable model, PEARNN, which integrates a self-attention mechanism and positional encoding for predicting the remaining useful life of bearings. The model combines the advantages of self-attention mechanism and positional encoding to enhance its understanding of sequential data through patch masking and periodic positional encoding. Experiments conducted on the XJTU-SY and IEEE PHM 2012 datasets validate the effectiveness of the model and visualize the attention regions of the signals. The results demonstrate that the proposed PEARNN model performs excellently in bearing fault prediction tasks, exhibiting good generalization and robustness. Additionally, the paper illustrates how the model identifies critical parts of the input signal through visualized attention mechanisms to improve prediction credibility and interpretability. PEARNN not only enhances the accuracy of fault prediction but also strengthens the interpretability of the model.

Key words

self-attention mechanism / wind turbine / remaining useful life / interpretability / positional encoding

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Yu Ping, Ping Mengmeng, Ma Jialin, Cao Jie. INTERPRETABLE DEEP LEARNING METHOD FOR PREDICTING REMAINING USEFUL LIFE OF WIND TURBINE BEARINGS[J]. Acta Energiae Solaris Sinica. 2025, 46(9): 66-75 https://doi.org/10.19912/j.0254-0096.tynxb.2024-0751

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