Gearbox fault diagnosis method based on multi-sensor data fusion and GAN

In response to the problem of the gearbox fault diagnosis and analysis based on multi-sensor data under dataset imbalanced conditions, a gearbox fault diagnosis method based on a kurtosis index data fusion and a generative adversarial neural networks (GAN) was proposed. This method weighted the fusion of multiple sensor data based on signal kurtosis, highlighting the fault sensitive components of the gearbox in the fused signal. Then, a wavelet packet transform was used to extract the energy coefficients of each frequency band of the signal as signal features. Finally, the classification and recognition of signal features were implemented based on a back propagation (BP) neural network. Due to the fact that in actual working conditions, fault signals were more difficult to obtain than normal signals, GAN was used to expand the fault data section of the dataset, and the expanded dataset was used to train BP neural network. Through test analysis, it is shown that the fault accuracy of the described method is as high as 98%, which verifies the correctness of the proposed method and provides new ideas and methods for multi-sensor data fusion and fault diagnosis problems.