Comparative Analysis of Random Forest and LSTM for Predictive Maintenance of Electric Motors - Muhammad ‘Atiq; Musab Ali El Nefati; Arief Marwanto; Fajar Husain Asy'ari; Rio Subandi; Danang Hendrawan (Journal of Fuzzy Systems and Control, 2026)
This study presents a comparative analysis of Random Forest and Long Short-Term Memory (LSTM) algorithms for predictive maintenance of electric motors using Industrial Internet of Things (IIoT) sensor data (current, temperature, vibration). A synthetic dataset from Kaggle comprising 5,000 samples across three operational states (HEALTHY, WARNING, CRITICAL) was employed. Experiments were conducted on Google Colab with a Tesla T4 GPU. The LSTM model achieved a classification accuracy of 98.9%, outperforming Random Forest (98.5%), with perfect precision and recall for the WARNING state and improved recall for the CRITICAL state (0.97 vs. 0.96). However, Random Forest demonstrated substantially shorter training time (2 seconds vs. 30 seconds). For real-time industrial deployment on Programmable Logic Controllers (PLCs), Random Forest is recommended due to its rapid inference (5-10 ms), while LSTM is better suited for critical assets where detection accuracy is prioritized. The findings also confirm the viability of Google Colab as an accessible platform for predictive maintenance research in academic settings. This work contributes a practical framework for selecting between machine learning and deep learning approaches in IIoT-based motor fault diagnosis.