Root cause analysis on changes in chiller performance using linear regression

Gas District Cooling (GDC) plants, designed to be environmentally efficient, require frequent maintenances, in order to avoid corrosions or leakages from the chemical reactions in Steam Absorption Chillers (SACs) of the plant. However, most of the plant experts face difficulty that the positive and the negative effects from the SAC maintenances are not clear. This is because there are various metrics to indicate GDC SAC performance, but they don't have enough information to describe chiller internal conditions. The paper describes a method to detect the root cause of the GDC SAC performance changes. Specifically, (1) the chiller performance is modeled by linear regression on the performance related sensor data, and (2) the root cause is determined by time series analysis of the sensor contribution ratios to the performance in accordance of the concept of theory of constraints (TOC). Evaluations in Universiti Teknologi Petronas (UTP) GDC plant showed that the method determined the root cause correctly in 3 cases out of 4 problem cases. Because the method determines the root cause only from the plant operation historical data without any inspections, it is generalized to detect component failures and other plant anomalies.