Abstract:
Objectives To address the issue of performance degradation in fault diagnosis of rotating machinery caused by noise interference in practical applications, a novel fault diagnosis approach based on Mel-frequency cepstral coefficients (MFCC) and parallel dual-channel convolutional neural network (PDCNN) is proposed. This method aims to improve the quality of fault feature extraction from vibration signals and enhance fault diagnosis capabilities.
Methods The MFCC is used to extract features from vibration signals contaminated by noise. Meanwhile, a novel parallel dual-channel convolutional neural network structure is designed. This network further explores the global features and deeper, finer details of the data, thereby enhancing the diagnostic performance of the method in strong noise environments.
Results Experimental evaluation results under different noise environments show that the proposed method achieves a fault diagnosis accuracy of over 98% in strong noise environments. Its noise robustness and diagnostic performance are significantly superior to other traditional methods.
Conclusions The research findings can provide valuable references for gearbox fault diagnosis in strong noise environments.
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