A Graph-Based Sensor Fault Detection and Diagnosis for Demand-Controlled Ventilation Systems Extracted from a Semantic Ontology

Fault detection and diagnosis in HVAC systems such as demand-controlled ventilation systems, is a crucial step to attain optimal user comfort and energy saving, through providing fast and accurate monitoring, control, and recovery solutions in buildings. This can be accomplished by increasing the number of the utilized sensors and actuators in each zone, which leads to an enormous increase in the complexity of the system, due to the need to capture these components' values and the interactions between them. To overcome the complexity issue, it is important to establish an accurate model of the system, which contains the system components like sensors and actuators, and the relationships between them. However, achieving an accurate model design of the building, its technical components and the relationships between them is rarely available. Especially, the lack of precision in technical measurements, which is needed in model-based diagnostic methods to develop precise thresholds and conditions that are required to make the decisions. In this paper a model was developed to overcome the previous challenges, by the following: 1) creating a simulated model for a building, to extract the missing sensors' thresholds, values and relationships between those sensors. 2) A semantic model represented by the building ontology, to model the relationships between sensors and their containing systems, created based on the diagnostic information provided by the simulated model. And 3) A novel diagnostic directed graph is extracted from the ontology to offer more automation to the diagnosis and lessen the complexity of the system, by providing a clear graph of the decision making process.