Vibration Representation and Speed-Joint Network for Machinery Fault Diagnosis Under Time-Varying Conditions With Sparse Fault Data

Abstract

Machinery fault diagnosis with limited fault data under time-varying conditions remains a persistent challenge. In practical industrial settings, healthy samples are generally abundant during daily monitoring, while fault data is sparse. This hinders further development of dynamic fault recognition technology. In this article, a novel self-supervised vibration representation and speed-joint network, i.e., VRSNet, is proposed to conduct few-shot fault diagnosis under time-varying speed conditions. First, contrastive learning (CL) is employed to achieve feature self-representation using only healthy vibration signals. This enhances the discriminability of extractor and facilitates the self-extraction of feature manifolds across varying speeds. Second, a dimension-lifting network named approximator (APP) is proposed to identify the potential relationship between rotational speeds and corresponding vibration representations, which facilitates condition alignment for each transient speed and cross-modal feature fusion. Finally, a vibration-speed-joint fine-tuning method is proposed to implement few-shot training for fault recognition, where the norm-freezing strategy effectively preserves the pretraining knowledge of both vibration and speed branches for generalization enhancement. VRSNet is verified through two cases under time-varying speed conditions. The results demonstrate that it outperforms those state-of-the-art (SOTA) methods and achieves a recognition accuracy of 84.75% in a 1-shot fault data scenario with varying speeds. Even when confronted with faults at previously unseen speeds, VRSNet can still recognize them accurately.