Bearing fault diagnosis based on vibro-acoustic data fusion and 1D-CNN network

Bearing fault diagnosis is an important part of rotating machinery maintenance. Existing diagnosis methods based on single-modal signals not only have unsatisfactory accuracy, but also bear the inherent risk of being misguided by single-modal signal noise. A new method is put forward that fuses multi-modal sensor signals, i.e. the data collected by an accelerometer and a microphone, to realize more accurate and robust bearing-fault diagnosis. The proposed method extracts features from raw vibration signals and acoustic signals, and fuses them using the 1D-CNN-based networks. Extensive experimental results obtained on ten groups of bearings are used to evaluate the performance of the proposed method. By analyzing the loss function and accuracy rate under different SNRs, it is empirically found that the proposed method achieves higher rate of diagnosis accuracy than the algorithms based on a single-modal sensor. Moreover, a visualization analysis is also conducted to investigate the inner mechanism of the proposed 1D-CNN-based method.

Automated summary

A new method utilizing fusion of multi-modal sensor signals has been proposed for more accurate and robust bearing-fault diagnosis. Experimental results show that this method achieves higher diagnosis accuracy compared to algorithms based on single-modal sensors.