Universal Domain Adaptation in Intelligent Fault Diagnosis via Simulating Unseen Classes

Since the introduction of domain adaptation to fault diagnosis, it has greatly improved the performance of cross-domain fault diagnosis. However, previous methods require constructing independent frameworks for specific domain adaptation scenarios in fault diagnosis, which reduces efficiency and increases consumption. The difficulty lies in the fact that the classes of source and target domains in universal domain adaptation scenarios are not uniform and agnostic, leading to the inability to identify unknown fault modes. To address this issue, we propose a novel Simulating Unseen Classes (SUC) method. It is a general framework that does not depend on a priori knowledge of the classes of target domains. More data with new classes are generated to simulate the unknown fault classes based on source domain at both data and feature levels. Class gap and domain gap between source and target domains are effectively reduced to extract domain invariant features. Adequate experiments are conducted on three benchmark datasets and the results demonstrate that our method can outperform other methods by a large margin. Note to Practitioners—Intelligent fault diagnosis plays an important role in automation system. In practice, the changes in equipment, operating conditions and the environment raise the difficulty of cross-domain fault diagnosis. This paper proposes a novel SUC method for universal domain adaptation in cross-domain fault diagnosis. It is a more general but difficult scenario where the prior of source and target domains is agnostic. More data with new classes is generated effectively based on source domain at both data and feature levels. Class gap and domain gap are reduced to improve the accuracy of fault diagnosis. Our method provides a universal framework that can be readily applied to different domain adaptation (DA) scenarios, including closed set DA, partial DA, open set DA, and universal DA. Experimental results demonstrate the superiority of our method. In the future, we will investigate more challenging issues in fault diagnosis, such as scenarios where the data in the target domain is unavailable during training.