Re-Masked AE-KD: A Physics-Informed Knowledge Distillation Method for Robust Machinery Fault Diagnosis

The poor generalization capability of deep diagnostic models remains the primary impediment to their deployment in safety-critical industrial systems. This paper proposes a novel re-masked autoencoder knowledge distillation (Re-masked AE-KD) method for robust machinery fault diagnosis, i.e., the models are subjected to testing using data from unseen operating conditions. Specifically, we develop a self-supervised re-masked convolutional autoencoder (RCAE) that employs dual-stage masking, applied to both input features and latent representations, to enhance learning of compressed yet distortion-invariant embeddings. Furthermore, we construct a physically-informed multi-scale filter (PIMSF) that encodes prior knowledge of vibration signal amplitude modulation characteristics for precise fault extraction, while integrating temporal-context encoding to capture time-varying dependencies. Finally, we propose a knowledge distillation method, in which a teacher model processes physically meaningful multi-domain features. Crucially, the teacher and the student models interact within a shared latent space and a bidirectional feature reconstruction process, enforcing consistency to preserve critical diagnostic information. Extensive comparative experiments demonstrate superior performance over several state-of-the-art methods under unseen operational conditions.