Fault Diagnosis of Motor Bearing Transmission System Based on Acoustic Feature Fusion

Bearings are crucial components in motor bearing transmission systems because they reduce friction and support loads. Therefore, bearing fault diagnosis is particularly important. This paper proposes a fault diagnosis method for motor bearing transmission systems based on acoustic signals and acoustic feature fusion. The complete acoustic signal is segmented, and seven time-series imaging methods, including Gramian Angular Difference Field (GADF) and Gramian Angular Summation Field (GASF), are used to convert one-dimensional signals into two-dimensional feature maps. The generated images are then input into a RegNet-based transfer learning network. According to the single-feature training results, the feature map datasets ranking in the top two, three, and four are selected for feature fusion to construct new datasets. The results obtained under the present experimental setup indicate that acoustic feature fusion can improve the diagnostic performance compared with using a single feature map dataset. After comprehensive comparison, the dataset generated by summing two feature maps, namely STFT and Mel spectrogram, is selected as the final input dataset in this study. The current work focuses on a fixed operating condition, and further validation under different speeds, loads, sensor positions, background noise levels, bearing models, and defect severities will be conducted in future work.