Masked Autoencoder via End-to-End Zero-Shot Learning for Fault Diagnosis of Unseen Classes

This study confronts a demanding fault diagnosis task where no sensor signals from the target faults can be utilized for model training. Considering semantic descriptions of the target (or unseen) faults may be known in advance, zero-shot learning (ZSL) is employed to solve the zero-sample fault diagnosis task. Most of existing ZSL methods designed for computer vision tasks rely on preextracted features generated by a powerful feature extractor. However, collecting a large amount of fault data in advance for training a powerful and universal fault feature extractor is impractical. To overcome this problem, we propose a masked autoencoder via end-to-end ZSL (MAE_EZSL) approach consisting of four steps, which are self-supervised MAE pretraining, shared latent space learning, zero shot classifier training, and unseen faults detection. The essence of our approach lies in the comprehensive utilization of MAEs to deeply explore the features of seen faults. Subsequently, we align the distributions learned from sensor signals and fault semantic information to construct essential features associated with unseen faults. Experiments were meticulously conducted to assess the performance of the MAE_EZSL approach using datasets obtained from a benchmark bearing and a specialized test-rig. The obtained results demonstrate that the proposed MAE_EZSL method exhibits competitive performance when compared to state-of-the-art ZSL algorithms.