Abstract

Predictive maintenance shows significant impact in managing and controlling the utility systems of commercial buildings. Here in this paper, the predictive maintenance that is in line with Industry 4.0/ Quality 4.0 is applied. The chilled water system is highlighted in this research and the main goal is to reach out the most occurred faults in one of the chilled water system, which is cooling tower. An engineering management framework was used through three parts, set up, machine learning, and quality control. Having said machine learning, decision tree algorithm is used in this research and prediction model showed high prediction accuracy. During an empirical period, the most occurred faults were malfunctioning blowdown system, over current, and low water basin level.

Keywords

Faults, Predictive Maintenance 4.0, Commercial Buildings, Chilled Water System, and Cooling Towers