Deep learning-based partial transfer fault diagnosis methodology for electromechanical systems

Abstract

Recently, transfer learning technology has provided valuable solutions to problems that are present in machinery with industrial applications. Through the use of transfer learning, basic diagnostic problems have been well addressed, especially in scenarios in which the training and test data are from different distributions. However, there are scenarios that require further consideration, such as partial fault diagnosis. In this paper, a deep learning-based fault diagnosis methodology is proposed to address the partial fault diagnosis problem, in which the data from the unsupervised target domain represents a category subspace of the full machine-state-label space. Specifically, a domain adaptation with adversarial learning schemes is proposed to achieve partial domain adaptation. The experimental results on an electromechanical test bench suggest that the proposed approach offers a practical solution to this partial fault diagnosis problem.