A review of computing-based automated fault detection and diagnosis of heating, ventilation and air conditioning systems. (June 2022)

• Record Type: Journal Article
• Authors: Chen, Jianli; Zhang, Liang; Li, Yanfei; Shi, Yifu; Gao, Xinghua; Hu, Yuqing
• Is Part Of: Renewable & sustainable energy reviews. Volume 161(2022)
• Publication Date: 2022-06
• Subjects: Fault detection and diagnosis -- Heating -- Ventilation and air conditioning systems -- Machine learning -- Artificial intelligence -- Data-driven -- Physics-based modeling -- Computing algorithm; Renewable energy sources -- Periodicals; Power resources -- Periodicals; Énergies renouvelables -- Périodiques; Ressources énergétiques -- Périodiques; 333.794
• DOI: 10.1016/j.rser.2022.112395
• Languages: English
• ISSNs: 1364-0321

Abstract

Abstract: Faults in Heating, Ventilation, and Air Conditioning (HVAC) systems of buildings result in significant energy waste in building operation. With fast-growing sensing data availability and advancement in computing, computational modeling has demonstrated strong capability to detect and diagnose HVAC system faults, hence, ensuring efficient building operation. This paper comprehensively reviews the state-of-the-art computing-based fault detection and diagnosis (FDD) for HVAC systems. Overall, the reviewed computing-based FDD methods are classified as two major approaches: knowledge-based and data-driven approaches. We then identify multiple important topics, including data availability, training data size, data quality, approach generality, capability, interpretability, and required modeling efforts, along with corresponding metrics to summarize the most updated FDD development. Generally, the knowledge-based approaches are further divided as physics-based modeling, Diagnostic Bayesian Network, and performance indicator-based methods while data-driven approaches include supervised learning, unsupervised learning, and regression and statistics-based methods. State-of-the-art FDD development, remaining challenges, and future research directions are further discussed to push forward FDD in practice. Availability of fault data, capability of existing methods to deal with complex fault situations (such as simultaneous faults), modeling interpretability for data-driven methods, and required engineering efforts for physics-based methods are identified as remaining challenges in FDD development. Improving modeling fidelity and reducing modeling efforts are essential for applying physics-based methods in real buildings. Meanwhile, addressing fault data availability, increasing algorithm adaptability, and handling multiple faults are essential to further enhance the applicability of data-driven FDD approaches. Highlights: Important aspects of Fault Detection and Diagnosis of HVAC are identified. Knowledge-based and data-driven FDD techniques are summarized. Improving directions are modeling fidelity, engineering effort, and fault modeling. The lack of fault data and label is still a critical issue to facilitate FDD.