AUTHOR=Qi Xiaoqiao , Yang Yuance , Han Shukui , Bai Guangyu , Fang Nanxiang 
  
TITLE=Fault diagnosis of electromechanical systems considering noise suppression and multiscale signal features
  
JOURNAL=Frontiers in Mechanical Engineering
  
VOLUME=Volume 11 - 2025
  
YEAR=2026
  
URL=https://www.frontiersin.org/journals/mechanical-engineering/articles/10.3389/fmech.2025.1754564
  
DOI=10.3389/fmech.2025.1754564
  
ISSN=2297-3079
  
ABSTRACT=IntroductionIn the electromechanical system, the performance of a direct current brushless motor is determined by its rolling bearings, which play a decisive role in ensuring the safe and smooth operation of the entire system. Thus, fault diagnosis of these bearings is of paramount importance. However, existing methods for diagnosing faults often suffer from low accuracy, particularly under complex noise conditions.MethodsThis study proposes an innovative approach to fault diagnosis that enhances the accuracy and robustness of detecting faults in brushless direct current motor rolling bearings. To achieve this goal, this study first employs wavelet threshold denoising to suppress noise in motor current signals and performs multiscale feature fusion. Additionally, a fault diagnosis method is developed by integrating a convolutional attention mechanism.ResultsThe outcomes indicated that the proposed diagnostic method achieved a recall rate of 90.89% and a precision rate of 98.69%, both higher than those of the comparative methods. The suggested approach outperformed the comparison methods in all four fault categories, with diagnostic accuracy rates of 99.4%, 98.9%, 98.8%, and 99.3%.DiscussionThe findings of the experiments reveal that the proposed diagnostic method can effectively identify faults in rolling bearings of brushless direct current motors, providing a theoretical foundation for research in the field of electromechanical system fault diagnosis. The contributions of this research are in three aspects. First, the BLDCM rolling bearing current signal is reconstructed using a multiscale feature and wavelet threshold denoising. This significantly improves the signal quality and ability to extract fault features. Second, CBAM, residual network and Swin Transformer encoder are integrated into the fault diagnosis model. Compared with the existing methods, higher accuracy and precision are achieved. This study finally provides a solid theoretical foundation for further research in the field of electromechanical system fault diagnosis, particularly for BLDCM rolling bearing fault diagnosis under complex noise conditions.