The invention discloses a wind turbine generator gear case fault diagnosis method based on VMD and FA_PNN. Firstly, gear case vibration signals acquired by a sensor are subjected to de-trending processing, then, the processed gear case vibration signals are subjected to VMD variation modal decomposition under the condition of different decomposition numbers and penalty factors, k modal componentsare obtained with a Pearson's correlation coefficient method, singular value entropy, power spectral entropy, marginal spectral entropy and instantaneous energy spectral entropy of the k modal components are extracted from three angles of time domain, frequency domain and time-frequency domain, a feature vector matrix capable of describing operating states of a wind turbine generator gear case ina quantization manner is formed, and finally, test sample data are tested with well-trained firefly optimized probabilistic neural network FA_PNN, so that fault diagnosis of the wind turbine generatorgear case is completed. Classified recognition of faults of the wind turbine generator gear case is realized.