Fault injection framework for fault diagnosis based on machine learning in heating and demand-controlled ventilation systems

The main purpose of a heating, ventilation and air-conditioning system is to provide thermal comfort and acceptable indoor air quality through adequate natural ventilation by exchanging the building zone air with the outside fresh air, as an option. This air exchange during the cold seasons can be considered as a heating load for the heating system which increases the energy consumption of the heating system. The use of the demand-controlled ventilation system can be counted as a potential energy saving method by its automatic adjustment of ventilation, which modifies the amount of fresh air coming from outside and causes less energy consumption by less heating load, as a result. Faults in system components such as sensors and actuators can result in different types of failures and severe implications on the efficiency of the heating and demand-controlled ventilation system. However, tracing the component and system behavior back to the faults is a challenging task. This study demonstrates a successful approach for finding the nature, value, time of occurrence, and locality of these faults using a mapping from failures to faults with knowledge-based diagnostic techniques. We introduce fault models for online diagnosis using machine learning. These fault models are established for a heating and demand-controlled ventilation system with fault injection blocks design using a simulation framework based on MATLAB/Simulink.