使用CNN和双阶段特征选择进行可解释轴承故障分类的混合诊断框架
Mohamed Elhachemi Saouli1,2, Mostefa Mohamed Touba2, Adel Boudiaf3
1LESIA Laboratory of Research, University of Mohamed Khider Biskra, Biskra 07000, Algeria.
Sensors (Basel, Switzerland)
|October 29, 2025
概括
本研究引入了用于旋转机械故障诊断的混合框架,将深度学习与可解释方法相结合,以提高准确性和透明度. 该方法在CWRU轴承数据集上实现了100%的分类准确性,使可靠的工业应用成为可能.
科学领域:
- 机械工程 机械工程
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 深度学习,特别是卷积神经网络 (CNN),在基于振动的旋转机械故障分类方面表现出色.
- 深度学习模型的解释能力有限,这阻碍了它们在安全关键的工业环境中使用.
- 及时诊断故障对于系统可靠性和尽量减少停机时间至关重要.
研究的目的:
- 开发一种混合诊断框架,将CNN转移学习与可解释的监督分类相结合.
- 在故障诊断中提高预测准确性和模型透明度.
- 为工业环境提供可解释和可靠的故障诊断解决方案.
主要方法:
- 采用了使用变量分析 (ANOVA) 和转换特征重要性 (PFI) 的双阶段特征选择过程.
- 从预先训练的VGG19网络中提取了深度特征,并进行了改进.
- 为了模型的可解释性,使用了夏普利添加式扩展 (SHAP).
主要成果:
- 拟议的框架在Case Western Reserve University (CWRU) 携带的数据集上实现了100%的分类准确性.
- 双阶段的特征选择有效地减少了维度,提高了分类性能.
- SHAP分析提供了对影响力特征的洞察力,这些特征推动了故障分类.
结论:
- 混合框架成功地将高性能与用于故障诊断的透明决策相结合.
- 该方法显示出在工业环境中提供可解释和可靠的故障诊断的巨大潜力.
- 将可解释的方法与深度学习相结合,提高了人工智能在机器诊断中的实用性.
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