基于领域自适应的变工况轴承故障诊断

Bearing fault diagnosis in variable working conditions based on domain adaptation

针对轴承故障诊断中存在训练样本和测试样本分布不同及各类故障数据不平衡导致故障识别率低的问题,设计了一种基于改进残差网络(ResNet)的领域自适应故障诊断方法。在诊断网络第1层使用多维度卷积结构进行特征提取,得到不同维度的故障特征信息;在领域自适应层采用局部最大平均差异(LMMD)对齐源域和目标域的分布,获取更多细粒度信息;使用类平衡损失函数(CBLoss)解决不平衡数据的训练问题,以Adam优化网络实现故障诊断。实验结果表明,所提方法可在故障样本类别不平衡下有较高的诊断结果。在2个轴承数据集和采集的风力发电机数据上进行实验验证,结果表明,所提方法具有一定的优越性,在数据样本不平衡情况下,诊断性能优于深度神经网络和领域自适应网络等深度迁移学习方法,可作为一种有效的跨工况故障分析方法。

This research develops a domain adaptive fault diagnosis method based on an enhanced residual network(ResNet)to address the issues that the distribution of training samples and test samples differs in bearing fault diagnosis and the imbalance of different fault data results in a low fault recognition rate.First,the multi-dimensional convolution structure is used for feature extraction in the first layer of the diagnosis network to obtain fault feature information of different dimensions.Then,the local maximum mean difference(LMMD)is used in the domain adaptation layer to align the distribution of the source and target domains,to obtain more fine-grained information.Finally,the class-balanced loss(CBLoss)function is used to solve the training problem of unbalanced data,and the Adam optimization network is used to achieve fault diagnosis.The experimental findings demonstrate that,even in cases when fault sample categories are unbalanced,the enhanced approach suggested in this work can produce better diagnosis outcomes.Experiments are carried out on two bearing datasets and collected wind turbine data.The results show that the improved method has certain advantages,and its diagnostic performance is better than other deep transfer learning methods in the case of imbalanced data samples.It can be used as an effective cross-condition failure analysis method.