Abstract: Under actual operating conditions, noise and outliers will inevitably be mixed in the collected vibration signals of bevel gearboxes. At the same time, considering the difficulty of obtaining fault samples, a robustness imbalanced convex hull-based classification(RICHC) model is proposed for intelligent fault diagnosis of bevel gearboxes. According to the role of different samples in class distribution estimation, a confidence function is designed for RICHC to reduce the weights of outliers and noisy samples,which will make the boundary of the convex hulls more compact and improve the robustness. At the same time, an adaptive scaling strategy is constructed for RICHC to control the scaling of the convex hulls between different classes, and the scaling ratio is determined by the dynamic imbalance factor between the majority class and the minority class. Based on this strategy, a more accurate classification hyperplane will be obtained for RICHC to improve the unbalanced data processing ability. The effectiveness and applicability of the proposed method is verified on bevel gearbox fault data, and the experimental results show that compared with other models, the proposed method has stronger anti-interference ability against noise and outliers, and the method has more excellent class imbalance classification performance.

Key words: convex hull-based classification, robustness, class imbalance problem, fault diagnosis, bevel gearbox