Application of Improved Grey Wolf Optimization KFCM Algorithm in Fault Diagnosis of Wind Turbine Gearbox - Zheng Xiaoxia; Qian Yiqun; Wang Shuai; Zhao Kun (Jixie chuandong)
In order to accurately identify the known and unknown fault types，a new fault diagnosis method for wind turbine gearbox based on the kernel fuzzy c-means clustering（KFCM）is proposed. The initially cluster centers and the kernel parameter of the KFCM model are taken as optimization variables，and an improved grey wolf optimization algorithm is used to find the optimal centers. The introduction of Levy flight strategy and non-linear coefficient vector in the improved grey wolf optimization algorithm can improve the convergence speed and accuracy of the algorithm， and the clustering centers and kernel parameters can be obtained under the optimal classification results. Then，according to the similarity between the new sample and the centers in the kernel space，firstly whether the sample belongs to a known fault type is determined， and then the fault type is diagnosed. Finally，the effectiveness of the proposed method is verified by experiments simulating different fault types of wind turbine gearbox.