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

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Journal of Vibration and Shock ›› 2022, Vol. 41 ›› Issue (16) : 224-231.

WANG Ran1, SHI Ruyu1, HU Shenghan1, LU Wenbo2, HU Xiong1

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Abstract

Contact measurement is 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 is obtained by using microphone array; then, acoustic imaging is 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) is established, which is 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.

Key words

Acoustic imaging / bearing fault diagnosis / convolutional neural network / wave superposition method / Grad-CAM

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WANG Ran1, SHI Ruyu1, HU Shenghan1, LU Wenbo2, HU Xiong1. An acoustic fault diagnosis method of rolling bearings based on acoustic imaging and convolutional neural network[J]. Journal of Vibration and Shock, 2022, 41(16): 224-231

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