ENERGY LOSS REDUCTION IN INDUSTRIAL HVAC SYSTEMS THROUGH PREDICTIVE MAINTENANCE AND SENSOR INTEGRATION

Abstract

Industrial heating, ventilation, and air conditioning systems consume a substantial portion of facility energy and often experience hidden efficiency losses due to equipment degradation, airflow imbalance, sensor faults, and delayed maintenance. This paper examines the role of predictive maintenance and sensor-based monitoring in reducing energy loss in industrial HVAC systems. The proposed approach integrates continuous sensor data from temperature, humidity, airflow, vibration, pressure, and energy meters with predictive analytics to identify abnormal operating patterns before they develop into major failures. The results show that sensor monitoring improves fault visibility, while predictive maintenance reduces unnecessary energy consumption by enabling timely cleaning, calibration, component replacement, and operational adjustment. Compared with conventional scheduled maintenance, the predictive approach achieved lower energy use, fewer unplanned faults, improved fault detection accuracy, and better cost savings. The findings suggest that industrial HVAC performance can be significantly improved when maintenance decisions are based on real-time condition data rather than fixed inspection intervals. Overall, predictive maintenance supported by sensor monitoring provides a practical and scalable method for improving energy efficiency, reducing operational costs, and extending HVAC equipment life in industrial environments.