基于改进GAN的水力测功器轴承故障在线诊断方法
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Online Fault Diagnosis Method of Hydraulic Dynamometer Bearing Based on Improved GAN
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    摘要:

    基于数据驱动的轴承故障诊断方法已成为轴承故障诊断领域研究的重点,但由于水力测功器轴承故障情况极少,导致基于数据驱动的轴承故障诊断准确率低。针对上述问题,本文提出了一种基于改进生成对抗神经网络(Generative Adversarial Networks)的水力测功器轴承故障在线诊断方法,首先对生GAN训练方法进行改进,用改进的GAN交替训练判别器和生成器学习原始数据的分布特性,建立了水力测功器轴承故障数据增强模型得到合成数据。然后结合原始数据和合成数据训练得到基于SVM的轴承故障诊断模型。最后采用该轴承故障诊断模型实现水力测功器轴承故障在线诊断。仿真结果表明,所提出的故障在线诊断方法通过改进GAN增强训练极大提升了轴承故障诊断的实时准确率,并具有抗噪声干扰性强的特点。

    Abstract:

    Bearing fault diagnosis methods based on data driven have been considered as a research focus in the field of bearing fault diagnosis. However, it is low in the accuracy of bearing fault diagnosis based on data drive because the bearing fault of hydraulic dynamometer is rare. It leads to low accuracy of bearing fault diagnosis based on data-driven. To solve this problem, An on-line fault diagnosis method of hydraulic dynamometer bearing based on improved generative adversarial neural network (GAN) is proposed in this paper. Firstly, GAN training method is improved. The distribution properties of the raw data are learned by the discriminator and the generator, and alternately trained with an improved GAN. The data enhancement model of bearing fault of hydraulic dynamometer is established to obtain synthetic data. Then the bearing fault diagnosis model based on SVM is obtained by combining the original data and synthetic data training. Finally, the bearing fault diagnosis model is adopted to realize the bearing fault diagnosis of hydraulic dynamometer on line. Through the simulation results, the real-time accuracy of bearing fault diagnosis is greatly improved by the proposed online fault diagnosis method through improved GAN enhanced training. And it has the characteristics of strong anti-noise interference.

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何鹏,罗智浩,胡蓉,等. 基于改进GAN的水力测功器轴承故障在线诊断方法[J]. 科学技术与工程, 2024, 24(14): 5804-5811.
He Peng, Luo Zhihao, Hu Rong, et al. Online Fault Diagnosis Method of Hydraulic Dynamometer Bearing Based on Improved GAN[J]. Science Technology and Engineering,2024,24(14):5804-5811.

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  • 收稿日期:2023-04-21
  • 最后修改日期:2024-03-07
  • 录用日期:2023-11-11
  • 在线发布日期: 2024-05-30
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