基于数字双胞胎和深度学习的减速器故障诊断
Weimin Liu1, Bin Han1, Aiyun Zheng2
1College of Mechanical Engineering, North China University of Science and Technology, Tangshan, 063210, Hebei, China.
Scientific reports
|October 17, 2024
概括
一种新的故障诊断方法结合了数字双胞胎 (DT) 和深度学习 (DL) 以提高准确性. 拟议的故障分配 GAN (FDGAN) 实现了高性能,在故障检测方面表现优于现有模型.
科学领域:
- 工程 工程师 工程师 工程师
- 计算机科学 计算机科学
背景情况:
- 传统的故障诊断方法与复杂的系统和有限的数据作斗争.
- 数字双胞胎 (DT) 提供高保真模拟,而深度学习 (DL) 在数据挖掘方面表现出色.
- 整合DT和DL为先进的故障诊断提供了一个有希望的途径.
研究的目的:
- 提出一种新的故障诊断方法,将数字双胞胎 (DT) 高保真行为和深度学习 (DL) 数据挖掘集成在一起.
- 开发和验证一个故障分布生成对抗网络 (FDGAN) 来映射虚拟和物理实体.
- 为了提高模型的稳定性,特征提取,并在故障诊断中解决网络退化.
主要方法:
- 开发了一个故障分布生成对抗网络 (FDGAN),利用数字双胞胎 (DT) 和深度学习 (DL).
- 来自已建立的测试平台的数据被用于训练和绘制虚拟和物理实体的地图.
- 使用MobileViG模型进行验证和故障诊断.
主要成果:
- 拟议的FDGAN实现了高诊断准确度,在800个训练样本中达到99.5%.
- FDGAN超越了其他方法,包括CycleGAN (98.86%),CACGAN (94.92%),ACGAN (86.45%),ML1D-GAN (82.33%) 和转移学习 (99.38%).
- 该方法在解决网络退化,有限的特征提取和模型强度不足方面表现出有效性.
结论:
- 通过FDGAN集成DT和DL,为准确的故障诊断提供了一种强大的方法.
- FDGAN创新的网络结构和培训方法提供了卓越的性能和稳定性.
- 这种方法有效地解决了复杂系统故障检测和分析的挑战.
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