AHU, FDD, Bayesian Network, Expert Rules
Air handling unit (AHU) is one of the most extensively used equipment in large commercial buildings. This device is typically customized and lacks quality system integration, which can result in, hardwire failures and controller errors. Air handling unit Performance Assessment Rules (APAR) is a fault detection tool that uses a set of expert rules derived from mass and energy balances to detect faults in air handling units. Although APAR has many advantages over other methods, for example, no training data required and easy to implement commercially, most of the time it is unable to provide the diagnosis of the faults. There is no established way to have the correct diagnosis for rule based fault detection system. In this study, we developed a new way to detect and diagnose faults in AHU through combining APAR rules and Bayesian Belief Network. BBN is used as a decision support tool for rule-based expert system. BBN is highly capable to prioritize faults when multiple rules are satisfied simultaneously. The proposed model tested with real time measured data of a campus building at University of Texas at San Antonio (UTSA).