HVAC systems account for approximately 40% of total energy usage in buildings worldwide, and interlinked HVAC units in built environments require a well-orchestrated maintenance strategy for efficient energy conservation efforts. The traditional approaches of maintenance — reactive, scheduled, and preventive — have limitations in accurately predicting issues arising from complex modern HVAC systems.
Predictive maintenance, using machine learning-led analytics, can predict equipment failure risks before the issue occurs, enabling precise tracking of HVAC equipment runtime, timely pre-failure interventions to ensure reliability, and reduced downtime.
The article outlines seven benefits of predictive HVAC technologies, including data-driven decision-making, reduced downtime, improved equipment performance, upgraded inventory management, energy optimisation, increased cost-effectiveness, and enhanced safety. We also discuss three predictive maintenance techniques for HVAC systems, which are vibration analysis, thermal imaging, and oil analysis, and how these techniques can work together to give a comprehensive picture of HVAC equipment health.
Heating, Ventilation, and Air Conditioning (HVAC) systems are the heart and soul of every building. Responsible for maintaining good indoor air quality (IAQ), HVAC systems, particularly air conditioners and electric fans, account for around 40% of the total energy usage in buildings worldwide. Additionally, in today’s Industry 4.0 world, HVAC units in built environments are increasingly interconnected, and a breakdown in one system affects the entire interlinked device ecosystem. An efficient and well-orchestrated maintenance strategy can result in significant benefits for HVAC systems, building management systems, and subsequently, energy conservation efforts as well.