Small Sample-Oriented Variable Working Condition Fault Diagnosis via Nondata-Enhanced Multicategory Contrastive Learning

In real-world industrial scenarios, production equipment and process systems usually operate under variable working conditions, and abnormal/faulty samples are hard to collect, bringing great challenges in implementing intelligent fault diagnosis. This work presents a novel data augmentation-free multicategory contrastive learning (MCCL) framework specifically designed for small sample-oriented fault diagnosis under variable working conditions. The contributions are twofold. First, a sample tuple batch construction strategy (STBCS) is proposed to establish sample tuple batches without resorting to data augmentation. This is achieved by leveraging positive sample pairs from all fault categories within the limited small-sample training set, thereby enhancing the diversity and representativeness of the training data. Second, an adaptive MCCL method (AMCCLM) is proposed based on the constructed sample tuples to learn high-quality domain-invariant features, thereby enhancing the generalizability and robustness of the fault diagnosis system. Extensive confirmatory and comparative experiments were carried out on two publicly available benchmark datasets: the spectra quest (SQ) and the Case Western Reserve University (CWRU) bearing dataset, and a real industrial application scenario: the axial flow fan fault diagnosis from a steel-making plant. The experimental results demonstrate the superiority and robustness of the proposed method compared to representative state-of-the-art methods, especially under conditions involving small samples and variable working conditions.