Contrastive Learning Based Collaborative Modeling of Heterogeneous Data for Few-Shot Fault Diagnosis

Few-shot diagnosis has received extensive attention recently. Existing methods rarely consider the consistency within and between heterogeneous data, leading to suboptimal diagnosis performance. To address this issue, a contrastive learning based collaborative modelling for few-shot diagnosis is proposed. First of all, a heterogeneous data enhancement workflows with distribution consistency assessment is designed to acquire sufficient industrial process information, which can also mitigate the inconsistency between enhanced data and original data. Following this, convolutional networks with customized structures are used to extract the multimodal features from heterogeneous signals. After that, the collaborative modelling and diagnosis module is devised through the joint optimization of contrastive loss and cross entropy loss, which can shorten the distance of similar samples in feature space and retain cross structure consistency. Finally, the effectiveness and superiority of the proposed method are substantiated through simulated and the real world cases.