Measuring Domain Shift in Vibration Signals to Improve Cross-Domain Diagnosis of Piston Aero Engine Faults

Abstract

Transfer learning is an effective approach to address the decline in generalizability of intelligent fault diagnosis methods. However, there has been a persistent lack of comprehensive and effective metrics for assessing the transferability of cross-domain data, making it challenging to answer the fundamental question in transfer learning: “When to transfer”. This study proposes a novel hybrid transferability metric (HTM) based on weighted correlation-diversity shift. The metric introduces a correlation shift measurement based on sparse principal component analysis, effectively quantifying distribution differences in domain-invariant features based on the sparse representation theory. It also designs a diversity shift measurement based on label space differences, addressing the previously overlooked impact of label variation on transferability. The proposed transferability metric is validated on four types of cross-domain diagnosis tasks involving piston aero engines. The results show that in diagnostic scenarios involving both supervised transfer learning and extreme class imbalance problems, HTM accurately predicted the transferability of the target tasks, which aligned with the actual diagnostic accuracy trends. It provides a feasible method for predicting and evaluating the applicability of transfer learning methods in real-world scenarios.