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@article{196513,
        author = {Vedant Prashant Buge and Umakant Shirshetti and Konjengbam Anand},
        title = {BetraX : IoT Based Smart Predictive Maintenance Module},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {3074-3085},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=196513},
        abstract = {Predictive maintenance has emerged as a critical strategy for enhancing the reliability and operational lifespan of electronic and industrial devices. Conventional maintenance paradigms, including scheduled inspections and reactive repairs, frequently result in unforeseen downtime and elevated operational costs. This paper presents a comprehensive AIdriven predictive maintenance framework that integrates Internet of Things (IoT) sensors, cloud computing infrastructure, and machine learning to enable continuous, real-time device health monitoring and proactive failure prediction. The proposed system employs temperature, vibration, and electrical current sensors interfaced with an ESP32 microcontroller, transmitting operational data to a Firebase Realtime Database at five-second intervals. A Python-based backend applies a Random Forest classifier to categorize device health into three discrete risk levels: LOW, MEDIUM, and HIGH. The framework achieves a macro-average F1-score of 94.3% with an 18.4-second early-warning lead time prior to HIGH-risk transitions. Comparative analysis against five existing frameworks confirms competitive accuracy and superior costefficiency. The paper further contributes mathematical formulations, algorithm pseudocode, deployment scalability analysis, and an Industry 4.0 contextual discussion.},
        keywords = {Predictive Maintenance; Internet of Things; ESP32; Random Forest; Firebase; Machine Learning; Cloud Analytics; Industry 4.0; Fault Detection; Real-Time Monitoring; IIoT.},
        month = {April},
        }