A self-supervised contrastive learning framework with the nearest neighbors matching for the fault diagnosis of marine machinery

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The problem of limited annotated samples is prevalent in the shipping industry, and it significantly deteriorates the performance of fault diagnosis based on data-driven approaches. In this paper, a self-supervised contrastive learning framework with the nearest neighbors matching (SCLNNM) is proposed to learn discriminative feature representation from large-scale unlabeled datasets for fault diagnosis. Due to the collected 1D signals of machinery different from 2D imagines, in addition to a designed reasonable composition of data augmentation to generate a similar instance for the 1D sequence, our scheme also finds the nearest neighbors in the support set as the positive instance of the input signal to increase the diversity of representations. In this framework, the 1D CNN model combined with contrastive learning is designed to learn robust and general representations from different augmented signals. On this basis, the limited annotated data is finally used to investigate what kind of feature representations are suitable, and train a simple classifier for the fault diagnosis. The collected engine dataset of an operational ship shows that the proposed framework can efficiently extract valuable feature information and improve classification accuracy under the limited annotated dataset.