Fault-tolerant control for outdoor ventilation air flow rate in buildings based on neural network

A supervisory control scheme for a sensor based demand-controlled ventilation system is described in this paper. The strategy based on neural network models is used to diagnose the measurement faults of outdoor and supply air flow sensors, and makes the fault-tolerant control of outdoor air flow when faults occur. Tests using that dynamic system simulation have been conducted to validate the strategy.