基于ACGAN和模型融合的电机轴承故障诊断方法

电机轴承疲劳试验成本较高和故障数据不足导致利用机器学习等人工智能算法进行故障诊断时效果不佳。另外，单一模型对电机轴承故障诊断的准确率也较低。为解决这两个问题，提出了一种结合辅助分类器生成对抗网络（ACGAN）和模型融合的电机轴承故障诊断方法。首先将采集到的振动数据转换为二维灰度图，对每个灰度图添加标签后输入ACGAN模型，生成大量与原始数据高度拟合的新样本。然后将新样本与原始样本混合，经数据降维后输入由6个基学习器和1个元学习器融合而成的模型中。最后由融合模型输出诊断结果。试验证明，ACGAN和模型融合能有效提高电机轴承故障诊断的准确率。

Fault Diagnosis Method of Motor Bearing Based on ACGAN and Model Fusion

Due to the high cost of motor bearing fatigue experiment and insufficient fault data, the effect of using artificial intelligence algorithms such as machine learning for fault diagnosis is not good. In addition, the accuracy of a single model for motor bearing fault diagnosis is also low. In order to solve these two problems, a fault diagnosis method for motor bearing fault diagnosis is proposed, which combines auxiliary classifier generative adversarial network (ACGAN) with model fusion. Firstly, the vibration data collected are converted into two-dimensional grayscale images, and each grayscale image is labeled and input into ACGAN model to generate a large number of new samples that highly fit the original data. Then the new samples are mixed with the original samples, and the data are dimensionality reduced to input into the model which is a fusion of six base-learners and one meta-learner. Finally, the diagnosis results are output by the fusion model. The test proves that ACGAN and model fusion can effectively improve the accuracy of fault diagnosis of motor bearing.