Fault Diagnosis of HVAC Air-Handling Systems Considering Fault Propagation Impacts Among Components

In a heating, ventilation, and air conditioning system, an air-handling system is a key module. Its components (e.g., air handling unit, air-mixing box, and fans), linked through airflows, condition air to a desired temperature and/or humidity based on comfort or controlled environment requirements. Identifying failure modes and estimating their severities allow maintenance crews to know which faults have occurred, how critical they are, and be guided in the repair process to improve the system availability. The problem of fault detection and diagnosis in air-handling systems is complex because of fault propagation across components, and high false alarm rates caused by uncertainties in system and measurement dynamics. In this paper, to capture fault propagation impacts in an efficient manner, dynamic hidden Markov models are developed to identify failure modes, since they contain state transition matrices depending on other components and do not generate joint states. To filter out false alarms, “coupled statistical process control” techniques are developed by using state transitions matrices representing coupling among components. Experimental results show that the method can effectively diagnose faults with high-diagnosis accuracy. Note to Practitioners-Faults in heating, ventilation, and air conditioning air handling units (AHU) may cause high energy consumption and discomfort to occupants. Fault diagnosis in AHU is challenging since: 1) effects of faults propagate across components connected by airflows and 2) measurement noises may cause high false alarm rates. In this paper, a novel fault diagnosis method is established to identify failure modes and fault severities. This method explicitly considers the fault coupling among components. To reduce false alarm rates, new statistical process control techniques are developed to filter out false alarms. Experimental results show that our method can effectively diagnose faults with high diagnosis accuracy.