Adaptive bearing fault diagnosis using reference frequency and modified scalogram

Previous studies on the diagnosis of natural faults in rolling element bearings have primarily concentrated on detecting an increase in the frequency amplitude across a broad frequency spectrum within the measured signal when a fault occurs in the bearing. However, this study introduces an adaptive method centered on the conspicuous distinction in frequency amplitude between the shaft and bearing fault frequencies as the bearing fault progresses. Specifically, we propose a bearing fault diagnosis approach leveraging the shaft frequency amplitude as a constant reference independent of prevailing conditions, as it consistently maintains a relatively steady level regardless of the presence of bearing faults. Moreover, we devised a modified scalogram to enhance the diagnostic performance for bearing faults by consistently and prominently visualizing information pertinent to faults. We comprehensively assessed fault diagnosis accuracy between a convolutional neural network employing the scalogram and one employing the modified scalogram to verify the diagnostic performance of the adaptive method. This analysis yielded accuracies of 93.6% and 99.3%, respectively. Furthermore, the relationship between the bearing fault diagnosis results and the visualized fault-related information in the scalogram and modified scalogram was analyzed using gradient-weighted class activation mapping, providing evidence supporting improved fault diagnosis performance using the adaptive method.