Many buildings suffer from operational inefficiencies, leading to uncomfortable indoor environments, poor air quality, and significant energy waste. Developing automatic fault detection and diagnosis (FDD) tools in building energy systems is essential to mitigate these issues, reducing both energy waste and maintenance costs. Diagnostic Bayesian networks (DBNs), as probabilistic graphical models, offer a promising solution due to their interpretability, robustness to uncertainty, scalability, and flexibility. In this paper, the practical applications of DBNs for FDD in building energy systems are comprehensively reviewed. The generic modeling procedure is systematically examined and summarized, covering problem formulation, structure modeling, parameter modeling, and fault isolation and evaluation. Then, the paper provides insights into DBN modeling objectives, modeling types, diagnostic samples, and modeling software based on the 43 key relevant papers. Furthermore, the paper discusses practical challenges such as sensor configuration, baseline estimation, threshold determination, and expert knowledge integration. Finally, the recommendations are provided to guide further research, aiming to enhance DBN implementation for building energy systems in real-world scenarios, thereby supporting the transformation of the building service industry into a smart sector and ultimately improving building energy performance.

Energy waste in buildings can range from 5% to 30% due to faults and inadequate controls. To effectively mitigate energy waste and reduce maintenance costs, the development of Fault Detection and Diagnosis (FDD) algorithms for building energy systems is crucial. Diagnostic Bayesian Networks (DBNs), as graphical probability models, are particularly useful in scenarios where high-quality data is not always available. While many studies have focused on single fault detection using DBNs, the occurrence of multiple simultaneous faults is common, yet the versatility of DBNs in handling such cases is rarely explored. This study adapts a DBN, initially designed for single fault diagnosis, to perform simultaneous fault diagnosis Experiments were conducted on an air handling unit (AHU) in the Netherlands, using implemented simultaneous faults to test the model. The results suggest that the DBN can detect both single and multiple faults effectively.

This study investigates the diagnostic capabilities of a Diagnostic Bayesian Network (DBN) for air handling unit (AHU) components, particularly focusing on the heat recovery wheel (HRW) and heating coil valve (HCV). Unlike data-driven methods relying heavily on high-quality labeled data, this knowledge-based DBN is more suitable for real-world applications, where labeled faulty and normal data are hard to obtain. Notably, existing studies predominantly concentrate on developing DBN for AHU with recirculated air, neglecting thorough investigations into AHU with HRW, a prevalent system in North Europe and increasingly recommended post-COVID-19 for mitigating viral propagation. This paper presents a DBN setup with expert knowledge for an AHU with HRW, which is evaluated using experimental data from an office building in the Netherlands. The results show that the proposed DBN can successfully diagnose typical faults in HRW and HCV.

Fault detection and diagnosis (FDD) provides several interrelated benefits, including reducing energy waste, enhanced operational efficiency, and maintaining indoor comfort. The initial step in FDD is to detect deviations from normal or expected operation. However, establishing a reliable baseline can be challenging, especially when there is a lack of sufficient system documents or when complex control strategies are involved. This study investigates three feature selection methods for the baseline estimation: expert knowledge-based, correlation-based, and causality-guided, using heating coil valve control estimation as an example. These methods were tested in an office building in the Netherlands. The results show that while the correlation-based method achieved the best estimation, it may lead to false negatives due to features with reverse causality. This study aims to emphasize the necessity of causal analysis in the baseline estimation to achieve reliable FDD in buildings.

Automated fault detection and diagnostics (FDD) can support building energy performance and predictive maintenance by leveraging the vast amounts of data generated by modern building management systems. Diagnostic Bayesian Networks (DBN) offer a particularly promising approach due to their robustness, flexibility and scalability. However, FDD applications in whole building systems are rare, as they require the integration of different building subsystems, with their own potential faults and symptoms, which increases complexity and makes the resulting DBNs system-specific. In order to overcome these limitations, the 4S3F (four symptoms and three faults) method offers a simplified, adaptable framework for FDD implementation across building systems. In this paper, we implement the 4S3F methodology to a whole-building HVAC system in a case study office building located in the Netherlands. Our methodology uses generic, aggregated representations of individual subsystems within the building, such that FDD methods for specific subcomponents can later be incorporated where available. We first define aggregated building system groups (boiler group, chiller group, hydronic groups, ventilation groups, and end user groups) and subsequently define generic faults that can be detected with the existing sensor infrastructure. This simplified system representation is then used to define a DBN to isolate the most probable system-level faults that lead to building-level symptoms. By focusing on the whole building system, this work aims to provide the groundwork to incorporate occupant feedback and behavior in FDD.

In practice, automated energy performance fault diagnosis systems are seldom installed in HVAC systems. The main reason is that a specific Fault Detection and Diagnosis (FDD) setup is time-consuming and expensive because the existing methods are component-specific, not aligned with HVAC design practices, and not fully automated. 4S3F (four symptoms three faults) method, based on system engineering and Diagnostic Bayesian Networks (DBN), was proposed to decrease the gap between the design of HVAC systems for buildings and energy performance diagnosis, and proofs of concepts were tested on diverse parts of the HVAC system of one specific building. In order to test the further applicability potential of the method, it is necessary to expand these tests and to study possible problems arising in practice, like the lack of sensors installed in a specific system or practical difficulties in the construction of the 4S3F Bayesian network by HVAC or control. However, due to the small number of validations carried out on the environment, parameters, and installation process of this method still need further discussion and refinements. In this paper, we investigate how to construct the DBN for the quite generic AHU (Air Handling Unit) of a, with mechanical supply and exhaust, heating and cooling coils, and heat recovery. The paper describes the possible DBN's depending on the technical design and the measurement points. The diverse Bayesians networks are compared, and it is concluded that also, with a limited number of sensors, a diagnostic network can be set up. It is also concluded that step-by-step instructions would be needed to facilitate the work of HVAC engineers when setting up the diagnosis model.