基于子领域自适应的变工况下滚动轴承故障诊断

Fault diagnosis of rolling bearing under variable operating conditions based on subdomain adaptation

针对变工况环境下采集到的滚动轴承振动数据特征分布不一致及待诊断样本标签较难获取的问题,提出了一种子领域自适应的深度迁移学习故障诊断方法。首先,为充分利用卷积神经网络图像特征提取能力,将滚动轴承振动信号采用连续小波变换生成图像数据集;其次,源域与目标域通用特征提取采用改进图像集预训练的ResNet-50网络结构,子领域自适应度量引入局部最大均值差异(LMMD)准则,该度量准则通过计算目标域伪标签以匹配条件分布距离来进行子领域自适应,从而减小不同工况下的子类故障特征分布差异,提高模型诊断精度;最后,在两个公开变工况滚动轴承故障数据集上进行试验验证,结果表明,本文方法平均识别准确率为99%左右,并将其与不同诊断方法进行了对比分析,说明了本文方法的有效性与优越性。

Aiming at the problem of inconsistent feature distribution of rolling bearing vibration data collected under variable operating conditions and difficulty in obtaining the labels of the samples to be identified,a sub-domain adaptive deep transfer learning fault diagnosis method was proposed.Firstly,to make full use of the image feature extraction capabilities of the convolutional neural network(CNN),the rolling bearing vibration signal was used to generate an image data set using continuous wavelet transform(CWT).Secondly,the common feature extraction of the source domain and the target domain adopted the ResNet-50 model structure of improved image set pre-training,and the sub-domain adaptive metric introduced the local maximum mean discrepancy(LMMD)criterion.This metric is used for sub-domain adaptation by calculating pseudo-labels in the target domain to match the conditional distribution distance,thereby reducing the difference in the distribution of sub-categories of faults under different working conditions and improving the accuracy of model diagnosis.Finally,experiments on two public variablecondition rolling bearing fault data sets verify that the proposed method has an average recognition accuracy of about 99%.Compared with the results of different transfer learning methods,the effectiveness and superiority of the proposed method are demonstrated.