迁移学习和CNN的电机故障诊断方法

Motor Fault Diagnosis Method Based on Migration Learning and CNN

针对缺乏数据导致卷积神经网络(Convolutional neural networks,CNN)训练不佳的问题,以三相异步电机故障诊断为研究对象,提出了基于迁移学习和CNN结合的电机故障诊断方法。首先搭建了电机故障诊断实验平台,通过加速度传感器获取CNN模型带标签数据,通过训练获取预训练模型;然后结合迁移学习得到的预训练模型迁移到目标域,并通过对目标域的带标签数据进行训练以优化CNN参数;最终获得可以对目标域数据有着良好分类能力的新模型,从而实现目标域带标签数据稀少情况下的电机故障诊断工作。通过将该方法与传统CNN、变分模态分解(Variational modal decomposition,VMD)-支持向量机(Support vector machine,SVM)、VMD-K近邻(K nearest neighbor,KNN)以及VMD-BP神经网络等识别模型进行对比验证,结果显示本文提出的迁移CNN模型模式识别方法有更好的识别效果。

Aiming at the problem that the lack of labeled data will lead to poor training of convolutional neural network(CNN),a motor fault diagnosis method based on the combination of migration learning and CNN is proposed for three-phase asynchronous motor fault diagnosis.Firstly,an experimental platform for motor fault diagnosis is built,the label data of input CNN model is obtained by acceleration sensor,and the pre-trained model is obtained through training.Then,the obtained pre-training model is transferred to the target domain with transfer learning,and a small amount of labeled data in the target domain is cleared for training and fine-tuning parameters,and the CNN parameters are optimized by training the labeled data in the target domain.Finally,a new model with good classification ability for the target domain data is obtained,so as to realize the motor fault diagnosis in the case of scarce labeled data in the target domain.By comparing this method with ordinary CNN,variational modal decomposition(VMD)-support vector machine(SVM),VMD-K nearest neighbor(KNN)and VMD-BP neural network recognition models for validation,the results show that the pattern recognition method of migrating CNN model proposed in this paper has better recognition effect.