A statistical pattern analysis framework for rooftop unit diagnostics

Abstract

Rooftop units are the most common source of heating, cooling, and ventilation in small- and medium-sized commercial buildings. However, it has become apparent that only a small portion of these systems work efficiently or in accordance with the design intent. Operational faults are known to be one of the main reasons for such inefficient performance. A statistical framework for rooftop unit diagnostics is proposed. The proposed approach is not dependent on high-accuracy models. It has systematic solutions for measurement constraints and, importantly, does not have the limitation of using data measured in a steady-state condition. The proposed approach employs techniques from signal processing and time series analysis to evaluate the correlation among measuring parameters and assessing the presence or absence of faults in the system. The proposed approach is illustrated by analyzing the performance of rooftop units located at different retail stores to demonstrate how abnormal behaviors can successfully be detected and isolated.