Abstract: As a key component in permanent magnet motor,the product quality of arc magnet is susceptible to degradation due to internal defects.However,traditional acoustic vibration detection methods have revealed some inefficiencies in the face of fast and accurate inspection requirements,so it is of great practical importance to develop an efficient and intelligent detection method for internal defects in arc magnets.This paper combines the advantages of deep learning and proposes a convolutional neural network-based acoustic vibration detection method for internal defects of arc magnets.In this method,the one-dimensional acoustic vibration signal of the arc magnets is firstly converted into the two-dimensional acoustic vibration image,and then fed into a convolutional neural network designed for the signal characteristics for learning and training,to complete the autonomous learning from the acoustic vibration image and extract the features that can distinguish the presence or absence of internal defects.Finally,the corresponding features are identified by softmax.The experimental results of four types of arc magnet samples show that the proposed method can achieve 99.38% accuracy of internal defect detection of arc magnets,the detection time of a single arc magnet is less than 0.031 s and has a high robustness of the model.

Key words: Acoustic vibration image, Arc magnet, Convolutional neural network, Deep learning, Fault detection