From data scarcity to open-set generalization: A unified framework for robust rotating machinery fault diagnosis

Abstract

Unexpected failures in rotating machinery cause substantial economic losses and safety hazards in industrial systems, underscoring the practical need for reliable condition-based monitoring. Intelligent fault diagnosis must simultaneously address three real-world constraints in Industrial IoT deployments: severe data scarcity, dynamic distribution shifts, and the presence of previously unseen fault categories. This paper reviews a decade of methodological progress (2016–2026), tracing the development from unsupervised clustering and few-shot meta-learning to open-set single-source domain generalization. We propose a unified six-tier taxonomy classifying methods into data-level synthesis, metric/meta-learning, domain adaptation, domain generalization, open-set recognition, and an emerging category covering attention-based and self-supervised approaches. We examine the integration of wavelet-guided generative models with adversarial and prototype-aware alignment objectives, contextualize convergent findings on attention mechanisms and self-supervised pretraining that corroborate the role of structural frequency priors, and show that purely distributional alignment is insufficient under open-set conditions without latent-space structural constraints. Five open problems are identified: OS-SSDG, calibrated conformal thresholding, physics-informed class completion, zero-shot disentanglement, and foundation-model pre-training for IIoT. We conclude that Conformal Prediction combined with wavelet-structured representations offers a principled path toward bounded, interpretable fault diagnosis under realistic industrial constraints.