Vibration-Based Fault Diagnosis and Kinetic Energy Harvesting Optimization for Industrial Motors

This paper proposes a novel framework for fault diagnosis and kinetic energy harvesting optimization in industrial motors using vibration data. Leveraging datasets from Kaggle and IEEE DataPort, we employ SciPy for spectral analysis, Random Forest for classification, and LSTM for dynamic parameter adjustment. Key innovations include temporal modeling with LSTM, achieving up to $\mathbf{9 8. 4 8 \%}$ accuracy and $\mathbf{2 1. 6 6 \%}$ energy recovery efficiency gain. The approach supports sustainable manufacturing by reducing maintenance costs, enhancing energy efficiency, and validating through comprehensive experimental evaluations on real-world industrial motor datasets.