An Open-Set Semi-Supervised Contrastive Learning for Bearing Fault Diagnosis

Deep learning-based bearing fault diagnosis methods typically require a substantial amount of labeled data, which are expensive and time-consuming to obtain. Semi-supervised learning (SSL) provides a solution by leveraging both labeled and unlabeled data, but its performance can be compromised by the presence of unknown classes in real-world unlabeled data, reducing robustness and diagnostic accuracy. To address this issue, we propose open-set semi-supervised contrastive learning (OSCL), a novel framework that combines contrastive learning (CL) with open-set recognition. OSCL first utilizes contrastive pretraining to extract discriminative feature representations from vibration signals. Furthermore, it jointly optimizes an open-set classifier (to perform open-set tasks) and a closed-set classifier (to perform closed-set tasks) using both known and unknown class data. To further enhance feature representations, raw vibration data is processed using a multidomain fusion strategy that integrates short-time Fourier transform (STFT), continuous wavelet transform (CWT), and time-domain conversion (TDC). Meanwhile, tailored strong and weak augmentations are also applied to enhance the model’s effectiveness. Experiments on three benchmark datasets show that OSCL achieves state-of-the-art performance in both closed-set classification and open-set scenarios while remaining robust with limited labeled samples. The framework’s ability to generalize across datasets and handle unknown classes demonstrates its practical applicability in industrial settings.