风力机轴承剩余寿命预测的解释性深度学习方法

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

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风力机轴承剩余寿命预测的解释性深度学习方法

更新时间：2025-10-22

- 风力机轴承剩余寿命预测的解释性深度学习方法
- Vol. 46, Issue 9, Pages: 66-75(2025)
- 作者机构：
- 作者简介：
- 基金信息：
- DOI：doi:10.19912/j.0254-0096.tynxb.2024-0751
  CLC： TK83
- Published Online：14 October 2025，
  
  Published：2025
- 稿件说明：
移动端阅览
[J]. 2025, 46(9): 66-75.
DOI：

[J]. 2025, 46(9): 66-75. DOI： doi:10.19912/j.0254-0096.tynxb.2024-0751.

摘要

提出一种基于自注意力机制和位置编码的可解释性模型（PEARNN）

用于预测风力发电机轴承的剩余使用寿命。此模型结合了自注意力机制和位置编码的优势

通过分块掩码块和周期性位置编码来增强模型对序列数据的理解能力。实验部分在XJTUSY和IEEE PHM 2012这两个数据集上进行

验证了模型的有效性并可视化信号关注区域。结果表明

所提PEARNN模型在轴承故障预测任务中表现优异

具有良好的泛化性和鲁棒性。此外

该文还展示了模型通过可视化注意力机制如何关注不同的信号部分

从而提高预测的可信度和解释性。PEARNN模型不仅提高了故障预测的准确性

还增强了模型的可解释性。

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.

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