A robust fault detection and diagnosis strategy for multiple faults of VAV air handling units

Abstract Fault detection and diagnosis (FDD) is critical to reliable operation and energy efficiency of variable-air-volume (VAV) air-conditioning system. In this paper, a robust fault detection and diagnosis strategy is presented for multiple faults of air handling units by using the residual-based exponentially weighted moving average (EWMA) control chart method and the rule-based fault diagnosis method. Residual-based EWMA control chart with designing chart limits is used to detect faults of air handling units. In order to provide a level of robustness with respect to modeling errors, control limits are determined by incorporating uncertainty information into EWMA control chart. Furthermore, the rule-based fault diagnosis method is extended to isolate simultaneous multiple faults of VAV air handling units in the mechanical cooling operation mode. Twenty-six expert rules consisting of system behavior rules and energy related rules are used to diagnose twenty-two faults of air handling units. The fault detection and diagnosis strategy was validated by using the operating data from real VAV air-conditioning systems involving multiple artificial faults. The results of validation show that residual-based EWMA control chart with designing control limits is a robust tool of fault detection for air handling units. The rule-based fault diagnosis method presented is efficient to isolate multiple faults of air handling units.

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