Real-time bearing fault classification of induction motor using enhanced inception ResNet-V2

Abstract

The rolling bearing is a vital part used in different rotating electrical devices. Detecting defects in bearings is crucial for the safe operation of these machines. However, it is challenging to use Deep Learning techniques to identify bearing defects when the machine is not under load. To resolve this issue, this paper presents the Constant-Q Non-stationary Gabor Transform with enhanced Inception ResNet-V2, proposed for the early-stage classification of ball bearing faults in induction motors. The proposed model obtained the vibration images, i.e. time-frequency images of unfiltered vibration signals from the laboratory experimental setup. These images are applied to the proposed model, which classifies the ball bearing faults under various load conditions while adjusting its hyperparameter values instead of employing default ones. Furthermore, the model underwent training using k-fold method to assess its resilience with the use of optimal values obtained from hyperparameter tuning. The model is evaluated by performance metrics like F1-score, Recall, Precision, Confusion Matrix and Training time. The proposed model accomplished an average classification accuracy of 99.84% in low load and full load conditions within a few epochs. Ultimately, when compared to Inception-V4 and ResNet-50, which achieved 91.41% and 91.65%, respectively, the experimental findings unambiguously demonstrate the superior performance of the proposed model over both models.