Multi-label deep transfer learning method for coupling fault diagnosis

With the development of complexity and integration of machines, the multiple components of the system are prone to simultaneous failures and the multi-fault signals may be measured in limited sensors. This fault mode is defined as coupling fault in this paper and becomes more common and attracts great attention. Current coupling fault diagnosis approaches rely on labeled data under consistent working conditions. However, in practical varying working conditions, the data is difficult to label and the traditional methods perform poorly. To address the challenge problem, we utilize some related annotated data under other working conditions for auxiliary learning and abstract this problem as a multi-label transfer learning problem which is a new research topic in fault diagnosis. We proposed a novel deep transfer coupling fault diagnosis method from the global and local views to achieve the effect of transfer, which is the first attempt to address this practical problem as far as we know. At the global level, we used Maximum Mean Discrepancy (MMD) at multiple stages of the network to reduce the hypothesis space of the model. Furthermore, we design a special manifold framework with multi-level similarity to constrain the hierarchy of the distance of the sample and the hierarchy of the labels being consistent between the two domains, to further improve the effect of local alignment. The proposed method has high accuracy in multiple transfer tasks for both public and laboratory datasets, powerfully demonstrating its effectiveness.