Fault diagnosis of HVAC: Air Handling Units and Variable Air Volume boxes

Abstract

As major modules of Heating, Ventilation and Air Conditioning (HVAC) systems, Air Handling Units (AHUs) and Variable Air Volume (VAV) boxes are used to condition and circulate air to rooms. To save energy and satisfy human comfort and ventilation requirements, accurately and timely diagnosing their faults is critical. The problem, however, is challenging since (1) Hidden Markov Models (HMMs) can capture coupling among components, but many states are required to represent failure modes and fault severities; and (2) diagnosing faults of many VAV boxes leads to high computational requirements. In this paper, dynamic HMMs are used to identify state transitions of components since (1) component processes have long time constants; and (2) dynamic HMMs have various state transition matrices under different conditions, thus can capture fault propagations. Dynamic HMMs are used to identify failure modes but not fault severities to reduce computational effort. To diagnose faults of VAV boxes with low computational effort, faults are localized in all VAV boxes. Unscented particle filter and statistical process control are used to further localize faults down to individual VAV level and to assess fault severities. Experimental results demonstrate that the method can accurately diagnose dependent faults.