基于滚动轴承故障诊断的类间排斥松弛判别迁移学习

针对滚动轴承实际变工况条件使得新工况样本的类标签很难甚至无法获取，导致故障诊断精度较低问题，提出基于类间排斥力判别迁移学习(Inter-Class Repulsive Force Discriminant Transfer Learning, IRFDTL)的故障诊断方法。在所提出的IRFDTL中，构造了非负扩展松弛矩阵将严格二进制标签矩阵转化为扩展松弛标签矩阵以减少辅助域分类误差并提高IRFDTL的泛化能力；引入了联合分布差异减小辅助域和目标域之间的差异以更好地实现两域的跨域迁移学习；并构造了类间排斥力项来增大两域中某类标签子域样本到其它类标签子域样本之间的距离以促进类判别学习；最后，采用交替方向乘子法对IRFDTL的整体框架进行优化。由于以上优势，IRFDTL能在新工况样本的类标签不存在情况下，仅利用历史工况中的有标签样本来对新工况待测样本进行较高精度的类判别，因而基于IRFDTL的故障诊断方法能对新工况待测样本进行较高精度故障诊断。滚动轴承故障诊断实例验证了该方法的有效性。

Inter-class Repulsive Slack Discriminant Transfer Learning Based on Rolling Bearing Fault Diagnosis

In view of the problem that it is difficult or even impossible to obtain the class labels of new working condition samples under actual variable working conditions, which leads to the low fault diagnosis accuracy, a novel fault diagnosis method based on inter-class repulsive force discriminant transfer learning (IRFDTL) is proposed. In the proposed IRFDTL, a nonnegative extended slack matrix is constructed to transform the strict binary label matrix into an extended slack label matrix for reducing the classification error in auxiliary domain and improving the generalization ability of IRFDTL; moreover, the joint distribution difference is introduced to reduce the difference between auxiliary and target domains, which can better realize the cross-domain transfer learning between two domains; additionally, the inter-class repulsive force term is constructed to promote the discriminative learning effect by increasing the distance between one class subdomain samples and the other class subdomains in the two domains; finally, the whole framework of IRFDTL is optimized by the alternating direction multiplier (ADM) method. Due to the above advantages, the IRFDTL can use only the labeled samples under historical working conditions to perform high-precision class discrimination on the testing samples under new working conditions when there are no class labels of testing samples. Thus, the proposed IRFDTL-based fault diagnosis method can achieve precise fault diagnosis of the testing samples under new working conditions. The fault diagnosis instances of rolling bearings verify the effectiveness of the proposed method.