Lightweight bearing fault diagnosis network based on acoustic signal

Abstract Bearings are an essential part of rotating machinery, but they frequently fail, particularly in situations requiring high-strength load bearing and fast rotation speed. This research suggests a unique strategy based on acoustic signals to solve the issues of low accuracy and limited resilience in the current bearing failure detection methods. A lightweight fault diagnosis network, MS-GhostNet V3, is designed to enhance the performance and efficiency of bearing fault detection. Firstly, the acoustic signals produced by rotating machinery are collected using a linear microphone array, capturing the spatial distribution and phase characteristics of the acoustic signal. To improve fault characteristic expressiveness, the time–frequency domain feature map of the acoustic signal is obtained using the short-time Fourier transform. Then the MS-GhostNet V3 network is used to realize bearing fault diagnosis. MS-GhostNet V3 is composed of multi-scale convolution fusion module (MCFM), ghost bottleneck module (GBNM) and classifier. To obtain multi-scale information, the multi-scale convolution parallel structure creates the MCFM. The GBNM, integrating the ghost module with the spatial and channel synergistic attention module, facilitates a more comprehensive representation of fault characteristics across both spatial and channel dimensions. Consequently, this enhances defect identification precision. According to experimental results, the suggested approach yields accuracies of 96.19% and 96.88% on both private and publicly available datasets. These outcomes satisfy the requirements for real-time defect detection and lightweight operation. And compared to classical methods, it exhibits superior fault diagnosis performance, with enhanced accuracy and robustness under noise interference. The code is available at: https://github.com/xgli411/MS-GhostNet-V3 .