A new multiple mixed augmentation-based transfer learning method for machinery fault diagnosis

Abstract

Abstract With the continuous development of various industries, the diagnosis of industrial equipment faults has been receiving increasing attention in recent years. Considering the complex and variable working conditions, and the limited amount of fault data, transfer learning has become an effective solution for fault diagnosis. Data augmentation techniques, particularly generative adversarial networks, have achieved tremendous development within the field of transfer learning fault diagnosis. However, traditional data augmentation methods experience difficulty in extracting features conducive to fault diagnosis from fault data under complex operating conditions, particularly in the case of raw vibration data from bearings. Therefore, this study proposes a new multiple mixed augmentation-based transfer learning (MMATL) method for machinery fault diagnosis. First, an augmentation chain that dynamically adjusts data augmentation strategies in accordance with the model’s performance is constructed based on AutoAugment. Then, a multiple mixed augmentation strategy that integrates fault data into the augmented data from the augmentation chain to obtain enhanced data suitable for training is proposed. This strategy consists of multiple augmentations, augmentation mixing, and data mixing. Finally, experiments confirm the effectiveness of MMATL on the bearing datasets from the gearbox of the Chinese CRH380A high-speed train, the test rig at the University of Paderborn in Germany and the self-made bearing failure test platform. Results indicate that the method can adaptively extract features from fault data that are conducive to fault diagnosis under complex operating conditions.