'Penerbit Universiti Kebangsaan Malaysia (UKM Press)'
Abstract
Office buildings often consume high energy to sustain building operations such as HVAC systems. A lack of proper decision-making approaches and a lack of maintenance planning will cause higher operational costs. This paper proposes data analytics for air conditioner’s performance in laboratory by using Internet of Things (IoT)-based monitoring system to improve efficiency in facility maintenance. It provides a monitoring system, notification system and performance dashboard to enable data analytics. The data analytics methods used here are i) condition-based maintenance which includes thermal analysis and electrical analysis; and ii) Overall Equipment Effectiveness (OEE) approach. The pre-maintenance performance measured for AC-1 is adequate while AC-2 does not meet the requirement. After the reactive maintenance was performed on AC-2; there was a performance increment of 63.15%. Based on sensors data, it seems to correlate between current draw and low refrigerant. It aids facility maintenance for early failure detection, which helps in decision-making. The result from the OEE approach also suggested the same decision-making to schedule maintenance. Performance needs to balance out to leverage power consumption without hefty operational costs for maintenance strategies. In conclusion, the data analytics provide insight for the maintenance management to monitor and schedule preventive maintenance before air conditioner (AC) faults happen. Meanwhile, the modified OEE approach for ACs to measure performance takes into consideration speed to cool down air and cost to run the AC which has not been explored yet elsewhere
