Copyright © 2025 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

@article{176219,
        author = {Ankit Kumar and Ankur Pandey and Aviral Gupta and Harshit Tripathi},
        title = {A Novel Approach for Predictive Maintenance in Industrial Motors Using IoT and Machine Learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {11},
        pages = {5369-5376},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=176219},
        abstract = {This study explores IoT and ML-based predictive maintenance (PdM) for industrial motors, analyzing IQ and RPM data from 900 samples. Among tested algorithms (decision trees, random forests, SVM, neural networks), random forests achieved highest accuracy in predicting motor faults. The research highlights PdM's potential to cut downtime and costs, while addressing data quality and interpretability challenges. Future work focuses on multi-sensor fusion and edge computing.},
        keywords = {Predictive Maintenance Industrial Motors IoT Machine Learning Random Forest Feature Engineering Downtime Reduction},
        month = {April},
        }