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This paper evaluates SelF-Rocket for multi-class fault diagnosis in electrical and mechanical systems, introducing a multivariate extension and comparing it with ROCKET-based methods on benchmark datasets.
This paper introduces Spectral Aliasing Pretext (SAP), a self-supervised learning method for fault diagnosis in rotating machinery. By deliberately undersampling vibration signals and training a Transformer to reconstruct the original spectrum, SAP learns discriminative frequency-domain representations that achieve strong classification performance with limited labeled data.