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Abstract Fault diagnosis is a crucial technology for ensuring the reliable and efficient operation of industrial systems. With the advancement of industrial informatization and intelligence, fault diagnosis methods have the trend shifting from traditional signal processing to deep learning. However, traditional deep learning models are not suitable for industrial scenarios with limited labeled data, imbalanced categories. To address these challenges, this paper proposes a novel approach based on semi-supervised generative adversarial networks (SGANs) to systematically study the fault diagnosis of rolling bearings in the case of unlabeled samples and sparsely labeled samples. In this work, the vibration time-domain vibration signal of the bearing is firstly transformed into a spectrum signal through the fast Fourier transform. This transformed signal is then fed into the GAN model to extract multi-layer sensitive features, providing a deeper understanding of the underlying fault characteristics. Subsequently, the SGAN method utilizes unsupervised learning via spectral clustering algorithms to automatically classify fault patterns in industrial equipment. Furthermore, it enhances semi-supervised learning by incorporating limited label information through softmax functions, effectively discerning the authenticity of unlabeled data. For the effectiveness of SGAN for bearing fault diagnosis, two diverse datasets are utilized including the widely-used Case Western Reserve University dataset and data acquired from South Ural State University. Compared to alternative models, the results underscore SGAN’s robustness, achieving high recognition accuracy and clustering performance. The proposed methodology contributes to the advancement of fault diagnosis technologies by combining unsupervised and semi-supervised learning techniques.
Li et al. (Tue,) studied this question.