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Rolling bearing fault diagnosis is conventionally performed by vibration-based diagnosis (VBD). However, VBD is restrained in some cases because vibration measurement usually requires the contact with the machine. Acoustical-based fault diagnosis (ABD) has the advantage of non-contact measurement over VBD. However, ABD has received little attention and rarely applied in bearing fault diagnosis. In this paper, a new non-contact ABD method for rolling bearings using acoustic imaging and convolutional neural networks (CNN) is proposed. Firstly, a microphone array is used to acquire the acoustic field radiated by rolling bearings. Then, acoustic imaging is performed with the wave superposition method (WSM). The reconstructed acoustic images can depict the spatial distribution of the acoustic field, which add a new spatial dimension in the acoustic data representation for fault diagnosis and makes it possible to localize the sound sources. Finally, CNN is applied to accomplish bearing fault diagnosis, which can overcome the problems of handcrafted feature extraction in traditional ABD methods. Experimental results verify the effectiveness of the proposed ABD method. Comparisons with peer state-of-the-art ABD methods further validate that the proposed method can mitigate the drawbacks of the existing ABD methods, and obtain more accurate and reliable diagnosis results.

A Non-Contact Fault Diagnosis Method for Rolling Bearings Based on Acoustic Imaging and Convolutional Neural Networks