基于域适应与分类器差异的滚动轴承跨域故障诊断

基于数据驱动方法诊断滚动轴承故障时，不同工况下的数据特征分布差异会导致模型诊断性能严重下降.针对这一问题，提出了基于域适应与分类器差异的滚动轴承跨域故障诊断方法.首先利用卷积神经网络对带标记的源域样本和无标记的目标域样本进行特征提取;然后通过2个全连接分类器进行故障分类;最后通过分步优化分类损失、域最大平均差异损失和分类器差异损失，实现源域和目标域之间的域分布对齐，从而实现无标记目标域样本的故障诊断.实验结果表明，所提方法与主流的域适应方法相比具有更高故障诊断准确率，验证了该方法的合理性和可行性.

Cross-Domain Fault Diagnosis of Rolling Bearings Using Domain Adaptation with Classifier Discrepancy

When diagnosing rolling bearing faults based on data-driven methods， the discrepancy in data distribution under different operating conditions may result in severe degradation of model diagnosis performance.To handle this issue， a cross-domain fault diagnosis method of rolling bearing based on domain adaptation with classifier discrepancy was proposed.Firstly， the convolutional neural network was used to extract the features of the labeled source domain samples and the unlabeled target domain samples.Then， the features were classified by two fully connected classifiers.Finally， the classification loss， the maximum mean discrepancy loss and the classifier discrepancy loss were optimized step by step to align the domain distribution discrepancy between the source domain and the target domain so as to implement the fault diagnosis of unlabeled target domain samples.The experimental results showed that the proposed method has a higher fault diagnosis accuracy rate than the mainstream domain adaptation methods， which verifies its rationality and feasibility.