基于GAN样本生成技术的智能诊断方法

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振动与冲击 ›› 2020, Vol. 39 ›› Issue (18) : 153-160.

论文

基于GAN样本生成技术的智能诊断方法

马波1,2,3，蔡伟东1，赵大力4

作者信息 +

Intelligent diagnosis method based on GAN sample generation technology

MA Bo1,2,3，CAI Weidong1，ZHAO Dali4

Author information +

文章历史 +

摘要

基于数据驱动的设备故障智能诊断方法是监测设备健康状况的重要手段，然而实际应用中，难以获取到足量有效的故障数据训练智能诊断模型。鉴于设备健康状态数据充足和现有智能诊断方法故障机理利用不足，提出基于GAN（生成对抗网络，Generative Adversarial Networks）样本生成技术的智能诊断方法。健康状态数据反映了设备个性特征，故障机理反映了设备共性特征，基于两种特征融合，得到故障数据样本，通过对深度卷积神经网络的训练为设备个体构建个性化的智能诊断模型。采用来自CWRU轴承数据和实验台模拟故障数据进行的实验结果表明，该方法相比现有智能诊断方法无需真实故障样本，在变负载条件下实现了很高的诊断准确率，具有较好的变工况迁移能力。

Abstract

Intelligent fault diagnosis based data-driven is an important tool to monitor the health of equipment. However, in practical applications, it is difficult to obtain a sufficient effective fault data training intelligent diagnosis model. In view of the sufficient data of equipment health status and the underutilization of the existing intelligent diagnosis method, an intelligent diagnosis method based on GAN (Generation Adversarial Networks) sample generation technology is proposed. The health status data reflects the characteristics features of the device, and the knowledge of the fault mechanism reflects the common features of the device. Based on the fusion of the two features, the fault samples are obtained. By training the deep convolutional neural network, a diagnostic model is constructed for each device and realized fault diagnosis under different working conditions. The test results from the CWRU bearing data and the simulated fault data of the test bench show that the proposed method can achieve high diagnostic accuracy under variable load conditions and has better variation than the existing intelligent diagnostic methods, and have good working condition transfer ability.

导出引用

马波1,2,3，蔡伟东1，赵大力4. 基于GAN样本生成技术的智能诊断方法[J]. 振动与冲击, 2020, 39(18): 153-160

MA Bo1,2,3，CAI Weidong1，ZHAO Dali4. Intelligent diagnosis method based on GAN sample generation technology[J]. Journal of Vibration and Shock, 2020, 39(18): 153-160

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