In operation status monitoring as well as fault identification for rotating equipment， collected fault samples are mostly fault-free samples with few fault samples， and this imbalance in data distribution can have a serious impact on the accuracy of classifier identification. To address this problem， a minority sample data generation method is proposed based on Fourier transform and Pearson correlation coefficient optimization of generative adversarial neural network （FP-GAN）. Such method is able to improve the accuracy of fault diagnosis training and recognition by expanding the fault minority samples. Fourier transform is used for getting the single side spectrum of the signal frequency domain， and GAN network is used for generating the signal frequency domain. After that， the generated data is optimized by Pearson correlation coefficient to obtain data closer to the real data by inverse Fourier transform. The validation of simulation and experimental data show that the data samples generated based on FP-GAN can be better integrated with the existing actual data in terms of time-domain features， time-domain statistical features and classifier classification results， which can effectively solve the data imbalance problem.