Hierarchical Deep Learning for Bearing Fault Detection in BLDC Motors Using Time-Frequency Analysis - Ahmed K. Ali; Wathiq Rafa Abed (Journal of Electrical and Computer Engineering, 2024)
This paper presents new hierarchical image-based time-frequency convolutional neural network (HTFICNN) for sorted bearing fault detection in brushless DC (BLDC) motors. The HTFICNN combines three different time-frequency visualisation methods: scalogram, spectrogram, and Hilbert spectrum for the transformation of current and vibration signals into time-frequency information (TFI). We use three different convolutional layers to extract features from three generated TFIs. We detect the targets using three patterns generated by the convolutional layers, utilizing three deep network structures, each containing a softmax classifier that identifies one pattern out of three. Using a three-pattern approach, the hierarchical fusion classification method then realizes the final target decision. We tested the proposed HTFICNN with data from BLDC motors and found that it works better than other deep learning models to detect bearing faults. Moreover, this work contributes to the advancement of fault diagnosis and predictive maintenance for BLDC motors, with potential applications in other fault detection scenarios.