Fault Diagnosis of Wind Turbine Gears Based on IGWO-LSSVM Model

In wind power generation systems, gears are high-risk components with a relatively high failure rate in the total number of unit failures. The application of gear fault diagnosis technology can achieve early warning and maintenance of equipment faults, which has significant engineering practical significance for preventing major equipment damage, reducing maintenance costs, and ensuring production continuity. This article takes the gears of wind turbines as the research object, applies the improved LSSVM diagnostic model based on IGWO to gear fault diagnosis, and compares it with the SVM method to verify the effectiveness of IGWO-LSSVM. The recognition accuracy of four types of gear faults (normal, pitting, tooth breakage, and wear) reached 96.3%, and the recognition accuracy of different degrees of faults (normal, slight wear, and severe wear) reached 95%. Therefore, the gear fault diagnosis method based on IGWO-LSSVM proposed in the paper can effectively identify different types of gear faults and different degrees of wear.