一种基于领域自适应的跨工况滚动轴承故障诊断新方法

机械设备在运转时，不同状态下滚动轴承监测数据存在较大分布差异且难以获取带标签的数据，导致现有模型在变工况条件下故障诊断准确率低。针对滚动轴承跨工况诊断难题，本文提出一种基于领域自适应的跨工况滚动轴承故障诊断方法，该方法首先提取振动信号故障敏感特征，并将其嵌入Grassmann流形空间，避免直接进行特征变换导致特征失真；其次利用动态分布对齐自适应调整源域和目标域分布差异，并通过增加类内散度正则化项最小化类内距离，增加类的可分性；最后，根据结构风险最小化原则建立域不变分类模型。在不同工况下的滚动轴承数据集上与现有方法进行了比较实验，结果表明本文提出的方法能有效提高分类准确率。

A new method for fault diagnosis of rolling bearing under cross working conditions based on domain adaptive

When the mechanical equipment is running, the monitoring data of rolling bearing have large distribution differences and vibration signals with labels are difficult to be obtained under different conditions, which leads to the low accuracy of existing models. In order to solve the problem of cross working condition diagnosis of rolling bearing, this paper proposes a fault diagnosis method based on domain adaptive. Firstly, the fault sensitive features of vibration signal are extracted and embedded into Grassmann manifold space to avoid feature distortion in the migration process. Secondly, dynamic distribution alignment is used to adaptively adjust the difference of distribution between source domain and target domain. Moreover, by increasing the regularization term of within-class scatter, the distance within the class is minimized and the separability of the class is increased. Finally, according to the principle of structural risk minimization, the domain invariant classification model is established. Compared with the existing methods, the proposed method can effectively improve the classification accuracy.