Research on imbalanced sample abnormal detection method for planetary gear systems based on GGAN

To address the low accuracy and poor robustness of anomaly detection for planetary gear trains under asymmetric sample conditions, this paper proposes a generalized generative adversarial network (GGAN) method. The proposed approach integrates a generative adversarial network, an autoencoder, and a contrastive learning mechanism. By leveraging a multi-scale discriminator and a residual network to extract nonlinear feature discrepancies, and combining kernel density estimation to quantify anomaly probabilities, it significantly improves anomaly detection accuracy. Building on the GGAN-based anomaly detection method, a three-stage anomaly evaluation framework is developed. In the normal operation stage, the initial detection model is trained using only normal samples. In the early degradation stage, the detection threshold and model parameters are refined using a limited number of anomalous samples. In the severe degradation stage, multi-scenario data fusion and similarity analysis are incorporated to achieve robust evaluation. Experimental results demonstrate that the proposed method can provide theoretical support and practical reference for research and applications in related fields.