A significant part of worldwide energy consumption is used to provide a comfortable climate inside buildings.
This energy is mostly used by heating, ventilation and air-conditioning (HVAC) systems.
A part of this energy is currently wasted due to faults, such as incorrect control signals or component failures.
In the field of HVAC fault detection and diagnosis (FDD), methods are developed that aim to detect that a fault is present in the system, and consequently diagnose which fault this is.
However, most of the methods that are currently being developed are data-driven, which require large amounts of labelled data.
In practice, this data often is not available, leading to a lack of adoption of FDD methods by building operators.
Additionally, most of the FDD approaches proposed in the literature have not been tested in real-time, instead validation has been performed on already existing datasets.
In this thesis, an FDD method is proposed to diagnose part of the HVAC system, air-handling units (AHUs), in real-time.
Air-handling units are an important part of the HVAC system, responsible for a significant part of the energy consumption.
The method applies the four symptoms three faults (4S3F) framework, to construct a diagnostic Bayesian network (DBN) without relying on historical data for symptom detection or fault diagnosis.
This DBN is implemented in Python to diagnose a case study AHU in a Dutch university building.
The method was validated with fault experiments, which were diagnosed with diagnosis periods ranging from one 10-minute sample to three hours.
All of the faults that were introduced and included in the DBN were accurately diagnosed for all diagnosis periods.
For the AHU operation data of 2022 and 2023, mostly control and temperature faults were diagnosed.
Incorrect control of the heating coil valve was found in 25\% and incorrect control of the ERW in 11\% of the days considered.
Additionally, a deviation between the measured intended supply temperature of more than 1 $\deg$ was found for 79\% of the days in the dataset.
Finally, the application of the method for real-time fault diagnosis has also been demonstrated, by collecting sensor data through the data streaming platform Apache Kafka.
This data was consequently stored locally in the time-series database InfluxDB, which could be queried for performing the diagnosis.
The processing time of performing the diagnosis was approximately ten seconds, demonstrating the potential for performing diagnoses per sample.
Recommendations for future work include extending the DBN to diagnose faults related to the cooling coil and faults occurring outside of operation hours.
Additionally, the developed diagnosis method should be applied to other AHUs, to further research the generalisability of the DBN.
