基于深度在线迁移的变负载下滚动轴承故障诊断方法

Fault diagnosis method of rolling bearing under varying loads based on deep online transfer

针对变负载条件下滚动轴承源域与目标域中相同状态的数据特征分布差异性较大,目标域数据按照序列方式在线获取时,数据更新需重新训练模型的问题,提出一种深度在线迁移的CNN-ISVM (convolutional neural networks-incremental support vector machine)变负载下滚动轴承故障诊断方法.该方法运用短时傅里叶变换得到不同负载下滚动轴承振动信号的频谱图并构建数据集;使用源域数据建立CNN-ISVM预训练模型并保存模型参数;利用迁移学习将源域共享模型参数迁移至目标域CNN-ISVM模型训练过程中,快速建立分类模型;分类模型中的ISVM分类器在保留已学到知识的基础上,在线处理目标域新增数据,无需重新训练.经实验验证,所提出方法可实现数据按照序列方式采集的变负载下滚动轴承多状态在线分类,并具有较好的稳定性及较高的准确率.

For the problem that the data feature distribution of the same state in the source domain and target domain of rolling bearing is quite different under varying loads, and the model needs to be retrained for data update when the target domian data is obtained online in a sequential manner, a fault diagnosis method of rolling bearing under varying loads with the deep online transfer CNN-ISVM(convolutional neural networks-incremental support vector machine) is proposed. Short time Fourier transform is used to obtain the time-frequency spectrum of rolling bearing vibration signals under different loads, and the data sets can be constructed;the source domain data is used to build the CNN-ISVM pre-training model, and the model parameters are saved;transfer learning is used to transfer the source domain shared parameters to the target domain CNN-ISVM model training process to quickly establish the classification model;the ISVM classifier in the classification model retains the learned knowledge and processes the new data in the target domain online without being retrained. The experimental results verify that the proposed method can realize multi-state online classification of rolling bearings under varying loads with data collected in a sequential manner, and has better stability and higher accuracy.