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AI in HVAC fault detection and diagnosis: A systematic review
Recent studies show that artificial intelligence (AI), such as machine learning and deep learning, models can be adopted and have advantages in fault detection and diagnosis for building energy systems. This paper aims to conduct a comprehensive and systematic literature review on fault detection and diagnosis (FDD) methods for heating, ventilation, and air conditioning (HVAC) systems. This review covers the period from 2013 to 2023 to identify and analyze the existing research in this field. Our work concentrates explicitly on synthesizing AI-based FDD techniques, particularly summarizing these methods and offering a comprehensive classification. First, we discuss the challenges while developing FDD methods for HVAC systems. Next, we classify AI-based FDD methods into three categories: those based on traditional machine learning, deep learning, and hybrid AI models. Additionally, we also examine physical model-based methods to compare them with AI-based methods. The analysis concludes that AI-based HVAC FDD, despite its higher accuracy and reduced reliance on expert knowledge, has garnered considerable research interest compared to physics-based methods. However, it still encounters difficulties in dynamic and time-varying environments and achieving FDD resolution. Addressing these challenges is essential to facilitate the widespread adoption of AI-based FDD in HVAC.
AI in HVAC fault detection and diagnosis: A systematic review
Recent studies show that artificial intelligence (AI), such as machine learning and deep learning, models can be adopted and have advantages in fault detection and diagnosis for building energy systems. This paper aims to conduct a comprehensive and systematic literature review on fault detection and diagnosis (FDD) methods for heating, ventilation, and air conditioning (HVAC) systems. This review covers the period from 2013 to 2023 to identify and analyze the existing research in this field. Our work concentrates explicitly on synthesizing AI-based FDD techniques, particularly summarizing these methods and offering a comprehensive classification. First, we discuss the challenges while developing FDD methods for HVAC systems. Next, we classify AI-based FDD methods into three categories: those based on traditional machine learning, deep learning, and hybrid AI models. Additionally, we also examine physical model-based methods to compare them with AI-based methods. The analysis concludes that AI-based HVAC FDD, despite its higher accuracy and reduced reliance on expert knowledge, has garnered considerable research interest compared to physics-based methods. However, it still encounters difficulties in dynamic and time-varying environments and achieving FDD resolution. Addressing these challenges is essential to facilitate the widespread adoption of AI-based FDD in HVAC.
AI in HVAC fault detection and diagnosis: A systematic review
Jian Bi (author) / Hua Wang (author) / Enbo Yan (author) / Chuan Wang (author) / Ke Yan (author) / Liangliang Jiang (author) / Bin Yang (author)
2024
Article (Journal)
Electronic Resource
Unknown
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