Predictive Maintenance of AC using Multiple Parameters

This research work focuses on the implementation of a predictive maintenance system for air conditioning (AC) units, utilizing critical parameters such as temperature and humidity levels. The primary objective is to proactively monitor the health of AC systems and predict potential issues before they lead to failures. Data collected from sensors embedded in the AC units is analyzed using data analytics techniques to identify patterns indicative of deteriorating performance or imminent faults. The thresholds for high temperature and humidity levels are defined, and instances exceeding these thresholds trigger maintenance alerts. The system employs a user-friendly interface for real-time monitoring and feedback, allowing maintenance personnel to take timely actions. The expected outcomes include reduced downtime, increased energy efficiency, and an extended lifespan for AC equipment, contributing to improved reliability and user satisfaction. The predictive maintenance approach offers a more proactive and cost-effective strategy compared to traditional reactive maintenance practices.