Feature-Based Transfer Learning for Bearing Fault Recognition Without Available Fault Data

Abstract

Abstract Machine learning has demonstrated its effectiveness in fault recognition for mechanical systems. However, sufficient data for establishing accurate and reliable fault detection methods is not always available in real-world applications. Transfer learning leverages the knowledge learned from a source domain in order to bypass limitations in data availability and facilitate effective analysis in a target domain. For mechanical fault recognition, existing transfer learning methods mainly focus on transferring knowledge between different operating conditions which require training samples corresponding to all desired fault conditions from the target domain in order to realize domain adaptation. However faulted data in real applications is usually unavailable and impractical to collect. In this paper, a transfer learning-based cross-machine bearing fault recognition method is investigated. This new method sees domain adaptation take place without faulted data being available in the target domain, and thus alleviates data availability limitations. The effectiveness of the method is demonstrated in a case study in which the bearing diagnostic method is transferred from an electric motor to a wind turbine.