Motor fault classification using hybrid short-time Fourier transform and wavelet transform with vibration signal and convolutional neural network

The detection and classification of failures in electric motors are critical for ensuring operational integrity and financial sustainability in industrial equipment. Traditional methodologies for failure diagnosis have relied on analyzing electrical and mechanical data signatures across time, frequency, and time-frequency domains, necessitating extensive technical acumen and considerable time investment. To address these limitations, this study integrates artificial intelligence, specifically convolutional neural networks (CNNs), with traditional diagnostic signals, thus reducing the requirement for specialized knowledge and expediting the diagnostic process. Employing pre-processing techniques such as wavelet and short-time Fourier transforms, the proposed AI-based method effectively uncovers latent patterns within vibration data from electric motors under varied operational conditions. Our findings reveal that the method achieves optimal classification accuracy (100%) with both STFT and a combination of STFT and wavelet techniques, and slightly less with wavelet alone (98.5%). Additionally, the investigation into the method's resilience against noise affirms its robustness, with accuracies marginally affected even at high noise levels (up to 60%). This research highlights the efficacy and reliability of combining AI with signal processing techniques for fault diagnosis in electric motors.