The running sound of an asynchronous motor contains a lot of important information,and the effective extraction of acoustic features can be used for fault detection.Therefore,a fault diagnosis method of asynchronous motor based on audible sound signal was proposed.Taking the asynchronous motor as the experimental object,the Gammatone filter was used to extract the GFCC feature vector of the sound signal in a normal working condition and two abnormal working conditions;a convolutional neural network was used to classify and recognize the visualized feature quantity.The results show that compared with the two feature quantities extracted by using the Meir filter,the feature quantities extracted by using the GFCC have higher target recognition rate.