Fault diagnosis of wind turbine gearboxes using SABO-optimised VMD and CNN-SVM integrated model

Abstract

Aiming at the problems of wind turbine gearbox vibration signals with multi-frequency characteristics, difficulties in fault feature extraction and insufficient generalisation ability of traditional diagnostic models. In this study, a gearbox fault diagnosis method is proposed, which integrates variational mode decomposition (VMD) optimised by the subtraction-average-based optimiser (SABO) with a classification framework combining convolutional neural network (CNN) and support vector machine (SVM). First, the SABO algorithm is introduced to optimise the key parameters of VMD (modal number k and penalty factor α ), which overcomes the limitations of traditional empirical selection and simple optimisation algorithms. Second, CNN and SVM are fused to construct an end-to-end integrated diagnostic model, using CNN to automatically extract fault features in the intrinsic modal functions (IMFs) obtained from VMD decomposition, avoiding the tediousness and subjectivity of feature selection by manual and traditional methods, and then inputting these features into SVM for classification. Finally, using the gearbox data set of Southeast University, five fault types are diagnosed and classified by MATLAB simulation experiment platform. The results demonstrate that the model constructed in this paper achieves a diagnostic accuracy of 96.43%, significantly outperforming multiple mainstream comparative models. It exhibits excellent robustness and adaptability under both noisy interference and variable operating conditions, while maintaining high computational efficiency. This provides a reliable technical solution for intelligent fault diagnosis and predictive maintenance of wind turbine gearboxes.