Abstract: This article presents machine learning (ML)-based approaches for detecting and predicting motor faults, enhancing predictive maintenance capabilities.
Content Overview:

1. Data Acquisition Methods

• Vibration analysis, stator current/voltage signatures, and thermal imaging.

• Time-frequency domain feature extraction (FFT, wavelet transforms).

2. ML Algorithms

• Deep convolutional neural networks (CNNs) for bearing fault classification.

• Recurrent neural networks (RNNs) for stator winding degradation prediction.

3. Prognostics Framework

• Remaining useful life (RUL) estimation using particle filters.

• Uncertainty quantification in prognostic outputs.

4. Industrial Deployment

• Edge computing vs. cloud-based ML inference.

• Cybersecurity considerations for IoT-integrated motor systems.