A latent-dynamics self-supervised framework for limited-label fault diagnosis in cyclostationary vibration measurements

Cyclostationary signals define the operational dynamics of rotating machinery, yet their periodic–stochastic structure and the scarcity of fault labels hinder robust representation learning for practical condition monitoring. We introduce the Signal-World-Model, a self-supervised framework that offers a practical solution to vibration analysis by framing it as a latent-state transition problem. The architecture integrates a convolutional encoder with a non-overlapping receptive-field strategy and a predictive transformer to infer the underlying system dynamics without explicit signal reconstruction. Pretrained via a teacher–student–predictor paradigm, the model is adapted for fault diagnosis using minimal labeled data through a lightweight classifier. Experimental validation on a multi-stage centrifugal pump and the Case Western Reserve University bearing case study demonstrates consistent improvements over representative contrastive-based, reconstruction-based, and standard joint-embedding predictive architecture approaches, providing an effective pathway for machinery monitoring under severe data constraints.