国外文献 > 1.轴承 > 8. 系统与应用 > 系统集成
关键词搜索：
| 标 题： |
A bearing fault diagnosis method without fault data in new working condition combined dynamic model with deep learning
基于动态模型与深度学习的无故障数据新工况轴承故障诊断方法 |
| 作 者： |
Xu, Kun
Kong, Xianguang
Wang, Qibin
Yang, Shengkang
Huang, Naining
Wang, Junji |
| 关键词： |
Fault diagnosis [故障诊断]
Bearing dynamic model [ 轴承动态模型]
Generative adversarial network [ 生成对抗网络]
Fault order conversion [ 故障顺序转换]
Zero-shot in new condition [ 全新状态，零次使用]
ROLLING ELEMENT BEARING [ 滚动元件轴承]
VIBRATION RESPONSE [ 振动响应]
CONTACT FORCES [ 联系部队]
PART I [ 第一部分]
SIMULATION [ 模拟]
SPECTRUM [ 光谱]
SIGNALS [ 信号]
SYSTEM [ 系统]
EMD [ EMD] |
| 出版年： |
2022 |
| 期刊名： |
Advanced engineering informatics
高级工程信息学 |
| 年卷期： |
2022, vol.54 |
| 摘 要： |
Bearing fault diagnosis plays an important role in rotating machinery equipment's safe and stable operation. However, the fault sample collected from the equipment is seriously absent, which obstacles the establishment of the diagnostic model. In this paper, a novel false-real data synthesis method combined bearing dynamic model with a generated adversarial network is proposed to solve the problem of zero-shot in new condition. Firstly, the bearing dynamic model is constructed to simulate vibration data in different conditions. Secondly, the conver-sion model is trained by simulation data in different conditions, which will be employed to convert real-world data in the old condition into the conversion data in the new condition. Thirdly, the GAN model is trained by simulation data and real-world data in old condition and finetuned by simulation data and conversion data in the new condition. Finally, simulation data in the new condition are inputted to the finetuned GAN model to obtain generated data in the new condition, and the fault diagnosis model is trained by it. To validate the performance of the proposed method, various comparative experiments are carried out on one rolling bearing dataset. The results indicate that the proposed method can solve the problem of zero-shot in new condition with excellent diagnosis performance.
轴承故障诊断在旋转机械设备的安全稳定运行中起着重要作用。然而，从设备上采集的故障样本严重不足，这阻碍了故障诊断模型的建立。本文提出一种新颖的假真实数据合成方法，将轴承动态模型与生成对抗网络相结合，以解决新工况下的零样本问题。首先，构建轴承动态模型，用于模拟不同工况下的振动数据； 其次，利用不同工况下的仿真数据训练转换模型，该模型可用于将旧工况下的真实世界数据转换为新工况下的转换数据； 第三，使用旧工况下的仿真数据和真实数据训练生成对抗网络（GAN），并结合新工况下的仿真数据和转换数据进行微调； 最后，将新工况下的仿真数据输入到微调后的GAN模型中，生成对应的新工况数据，并基于此训练故障诊断模型。为验证所提出方法的性能，对一个滚动轴承数据集进行了多种对比实验。结果表明，该方法能够在新工况下有效解决零样本问题，并具有优异的诊断性能。 |
| 中文内容为网络AI翻译，专业术语未必准确，仅供参考。 |
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