An acoustic fault diagnosis method of rolling bearings based on acoustic imaging and convolutional neural network

Contact measurements generally used in the common vibration diagnosis techniques, which has certain limitations in situations where measurement is limited. In this paper, a rolling bearing acoustic fault diagnosis method based on acoustic imaging and convolutional neural network with the advantage of non-contact measurement was proposed. First, the spatial acoustic field radiated by the rolling bearing was obtained by using microphone array; then, acoustic imaging was performed by wave superposition method, and the reconstructed acoustic image can describe the spatial distribution information of the acoustic field; finally, a convolutional neural network (CNN) was established, which was trained for fault diagnosis using the acoustic image samples of different bearing operating states. Meanwhile, to address the problem of lack of interpretability of diagnostic results of deep learning models, this paper investigates the interpretability of convolutional neural networks in acoustic image-based bearing fault diagnosis using the gradient-weighted class activation map ( Grad-CAM) algorithm. The acoustic array data from the bearing experimental bench verifies the effectiveness and superiority of the proposed method. © 2022 Chinese Vibration Engineering Society. All rights reserved.