Automatic model-based fault detection and diagnosis using diagnostic directed acyclic graph for a demand-controlled ventilation and heating system in Simulink

Abstract

In green and smart buildings, the main purpose of a demand-controlled ventilation system is to prevent over ventilation which leads to energy saving through automatic adjustment of ventilation damper while it maintains thermal comfort for occupants and indoor air quality in an acceptable range. These complex systems include sensors and actuators. Several problems such as system failure or performance degradation can arise due to faults in each component. A major contribution of this paper is to test and implement diagnostic directed acyclic graph as the fault detection and diagnosis method in demand-controlled ventilation systems. The introduced simulation framework is a helpful operation platform for this means. In this study, different types of faults such as sensor faults including wrong sensor readings or noisy sensors, and actuator faults such as a stuck damper actuator or stuck heater actuator are modeled, simulated, and injected into the system. Based on the framework, fault detection and diagnosis using diagnostic directed acyclic graphs are proposed. The results show that better energy efficiency can be achieved with automatic fault detection and diagnosis.