在轴承故障诊断的可解释特征中应用C-InGAN模型
Wanyi Yang1, Tao Liang1, Jianxin Tan2
1School of Artificial Intelligence, Hebei University of Technology, Tianjin 300130, China.
Entropy (Basel, Switzerland)
|June 26, 2024
概括
本研究引入了一个条件解释的生成对抗网络 (C-InGAN),用于轴承故障诊断,提高信号特征的解释性. C-InGAN模型有效地从振动信号中提取和评估可解释的特征.
科学领域:
- 机械工程 机械工程
- 人工智能的人工智能
- 信号处理 信号处理
背景情况:
- 传统的故障诊断方法与提取的信号特征的解释性作斗争.
- 可解释的故障诊断对于可靠的机械健康监测至关重要.
研究的目的:
- 提出一种新的条件解释生成对抗网络 (C-InGAN),用于轴承故障诊断.
- 为了提高振动信号的诊断解释性.
主要方法:
- 振动信号的无化和频率转换.
- 使用带有隐藏代码的生成器 (G) 和带有分辨器层的分辨器 (D).
- 反对训练学习特征映射和提取可解释的编码.
主要成果:
- 该C-InGAN模型成功地生成了特定特征的模拟信号.
- 区分器 (D) 有效地评估振动信号中的可解释特征.
- 证明了离散和连续特征编码的有效分离.
结论:
- 拟议的C-InGAN模型在轴承故障诊断中提供了更好的解释性.
- 该方法通过实验案例验证有效性,增强机器诊断.
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