A knowledge-guided and data-driven method for building HVAC systems fault diagnosis

Fault diagnosis is crucial for energy conversation of building HVAC systems. Generally, knowledge-driven fault diagnosis methods have good interpretability, whereas data-driven fault diagnosis methods have high diagnosis accuracy. With the aim of integrating the advantages of both types of methods, this paper proposes a knowledgeguided and data-driven fault diagnosis method. The proposed method develops a diagnostic Bayesian network (DBN) based on both expert knowledge and operational data. A probabilistic framework is developed for determining the prior DBN structures based on expert knowledge. An improved genetic algorithm-based approach is raised for further optimizing the DBN structures based on the operational data. Local casual graphs are generated from the DBN for visually interpreting the fault action mechanisms. Experts can evaluate the reliability of the diagnosis results using the local casual graphs, and then make reliable decisions. The proposed method is evaluated using the experimental data from the ASHARE Project 1312-RP. The results show that the performance of the proposed method is promising. Six typical faults are interpreted by the local casual graphs. It is demonstrated that the local casual graphs can effectively reveal the action mechanisms behind the six faults.

Automated summary

Fault diagnosis plays a critical role in energy conservation of building HVAC systems. This paper proposes a knowledge-guided and data-driven fault diagnosis method that integrates the advantages of both knowledge-driven and data-driven methods, accurately interprets faults, and reveals fault mechanisms through local causal graphs.