According to the low efficiency and low accuracy of motor bearings fault diagnosis,a genetic radial basis function neural network optimization algorithm based on FastICA was proposed.The signal was divided into multiple independent signal sources by using independent component analysis algorithm;the independent eigenvectors were constructed by using the independent signal source;the separated independent signal source was taken as a sample and input to the radial neural network optimized by genetic algorithm for fault identification,and compared with other classification algorithms.The experimental results show that this algorithm has better fault diagnosis ability for multi-signal fault diagnosis of motor bearings.