A novel transfer learning method for robust fault diagnosis of rotating machines under variable working conditions

Abstract

Vibration signals are closely linked with health conditions of rotating machines and widely used in fault diagnosis. Unfortunately, traditional vibration signal-based fault diagnosis methods are under a universal assumption that the target vibration signals for application and the available vibration signals for model training are collected from the same distribution, which is always impractical in real-world scenarios due to working condition variation. For robust fault diagnosis under variable working conditions, although some transfer learning-based methods are proposed, they mostly aim at aligning only the marginal distribution discrepancy of datasets, which is validated not sufficient in some cases. Hence, we propose a new transfer learning method called improved joint distribution adaptation (IJDA) to align both the marginal and conditional distributions of datasets more comprehensively. Meanwhile, built on it, a working condition-robust fault diagnosis method is developed, which utilizes vibration signals and is mainly composed of three parts. Firstly, a new data augmentation method is developed to generate more useful samples for imbalanced vibration signals, which innovatively uses noise to boost network performance. Secondly, sparse filtering (SF) is employed to reduce the input dimension of IJDA. Finally, IJDA is utilized to firstly extract both sharing and principal features and then diagnose the features. Experiments on vibration signal datasets of roller bearings and a gearbox and comparisons with other methods verify its effectiveness and applicability.