基于DA-DCGANAN的液压失衡样本的故障诊断方法
Yang Zhao1,2, Anqi Jiang3, Wanlu Jiang1,2
1Hebei Provincial Key Laboratory of Heavy Machinery Fluid Power Transmission and Control, Yanshan University, Qinhuangdao, 066004, China.
Scientific reports
|July 2, 2025
概括
这项研究引入了一种双重注意力深度卷积生成对抗网络 (DA-DCGAN),以使用有限的数据改进机械故障诊断. 该方法提高了对轴承和液压的诊断准确性和模型概括性.
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:
- 机械设备状态监测和故障诊断对于运行安全至关重要.
- 有限和不平衡的故障数据显著阻碍了在现实场景中诊断模型的准确性和可靠性.
研究的目的:
- 为了应对机械设备诊断中不平衡的故障数据所带来的挑战.
- 提出一种用于生成故障信号和在不平衡条件下增强诊断能力的新方法.
主要方法:
- 断层振动信号被转化为时间频率图,使用连续波纹转换 (CWT).
- 开发了一种双重注意深度卷积生成对抗网络 (DA-DCGAN),用于生成用于数据增强的合成故障样本.
- 增强数据集被用来训练卷积神经网络 (CNN) 和双重注意力-CNN (DA-CNN) 分类器.
主要成果:
- DA-DCGAN方法显著改善了不平衡轴承和液压数据集的分类性能.
- 该方法证明了在各种不平衡比率中增强的诊断准确性和模型概括性.
- DA-CNN分类器显示了捕获少数类故障特征的改进能力.
结论:
- 拟议的DA-DCGAN是一种有效的解决方案,可以提高机械系统的故障诊断准确性和可靠性,这些系统的数据不平衡.
- 这种方法为改善诊断模型的泛化提供了一个强大的策略.
- 该研究强调了生成对抗网络在解决工业诊断中的数据短缺方面的潜力.
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