Gearbox Fault Diagnosis Based on Compressed Sensing and Multi-Scale Residual Network with Lightweight Attention Mechanism

Shihua Zhou (), Xinhai Yu, Xuan Li, Yue Wang, Kaibo Ji and Zhaohui Ren ()
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Shihua Zhou: School of Mechanical Engineering & Automation, Northeastern University, Shenyang 110819, China
Xinhai Yu: School of Mechanical Engineering & Automation, Northeastern University, Shenyang 110819, China
Xuan Li: School of Mechanical Engineering & Automation, Northeastern University, Shenyang 110819, China
Yue Wang: School of Mechanical Engineering & Automation, Northeastern University, Shenyang 110819, China
Kaibo Ji: School of Mechanical Engineering & Automation, Northeastern University, Shenyang 110819, China
Zhaohui Ren: School of Mechanical Engineering & Automation, Northeastern University, Shenyang 110819, China

Mathematics, 2025, vol. 13, issue 9, 1-21

Abstract: As a core component of mechanical transmission systems, gear damage status significantly impacts the safety and efficiency of an overall mechanical system. However, existing fault diagnosis methods often struggle to extract features effectively in complex application scenarios characterized by conditions such as high temperature, high humidity, and high-level vibrations. Consequently, they exhibit poor adaptability and limited anti-noise capabilities. To address these limitations and enhance the adaptability and precision of gear fault diagnosis (GFD), a novel compressive sensing lightweight attention multi-scale residual network (CS-LAMRNet) method is proposed. Initially, compressive sensing technology was employed to remove noise and redundant information from the vibration signal, and the reconstructed 1D gear vibration signal was then converted into a 2D image. Subsequently, a multi-scale feature extraction (MSFE) module was designed based on multi-scale learning, with the aim of improving the feature extraction ability of the signal in noisy environments. Finally, an improved depth residual attention (IDRA) module was established and connected to the MSFE module, further enhancing the exactitude and generalization ability of the diagnosis method. The performance of the proposed CS-LAMRNet was evaluated using the NEU dataset and the SEU dataset, and it was compared with seven other fault diagnosis methods. The experimental results demonstrate that the accuracies of the CS-LAMRNet reached 99.80% and 100%, respectively, thus proving that the proposed method has a higher fault identification capability for gears under noisy environments.

Keywords: CS-LAMRNet; fault diagnosis; multi-scale feature extraction; attention mechanism; Gramian angular difference field (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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