Attention-based deep meta-transfer learning for few-shot fine-grained fault diagnosis

Deep learning-based fault diagnosis methods have made tremendous progress in recent years; however, most of these methods are coarse grained and data demanding that cannot find the root causes of mechanical system failures at a finer granularity with limited fault data. Therefore, in this study, we first investigate the few-shot fine-grained fault diagnosis (FSFGFD) problem, with the aim of identifying novel fine-grained faults under different working conditions using only few samples from each class. To address the difficulties of fine-grained fault feature extraction and poor model generalization to unseen few-shot faults in FSFGFD tasks, a novel attention-based deep meta-transfer learning (ADMTL) method is proposed. First, the failure modes under different working conditions are considered as fine-grained faults, and their raw signals are transformed into time-frequency images. Based on this, an attention mechanism is introduced to guide the feature extractor of the ADMTL on what information to learn. The ADMTL then follows a three-stage learning process of pre-training, meta-transfer, and meta-adaptation to achieve fast adaptation to new fine-grained faults using a priori knowledge gained from known faults. Furthermore, a parameter modulation strategy is employed to adaptively update the pre-trained network during the meta-transfer process. The comprehensive experimental results of three case studies demonstrate the superiority of our method over state-of-the-art methods. The proposed method achieves excellent performance with an average accuracy of 99.08%, 95.86%, and 77.74% for FSFGFD tasks when performing meta-transfer within the same machine and between different machines, respectively.(c) 2023 Elsevier B.V. All rights reserved.