Motor Health Monitoring

Motor health monitoring aims to detect and classify motor faults early, preventing costly downtime and improving industrial efficiency. Current research heavily utilizes deep learning, particularly convolutional neural networks (CNNs), applied to various signal types (vibration, current, sound) often pre-processed with techniques like Short-Time Fourier Transforms or Wavelet Transforms to extract relevant features. These methods show high accuracy in identifying faults like bearing damage, short circuits, and overloads, surpassing traditional machine learning approaches. The resulting advancements have significant implications for predictive maintenance, enabling more targeted interventions and reducing operational costs across various industries.