Journal article
A review of data-driven fault detection and diagnostics for building HVAC systems
Applied energy, v 339, 121030
01 Jun 2023
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
With the wide adoption of building automation system, and the advancement of data, sensing, and machine learning techniques, data-driven fault detection and diagnostics (FDD) for building heating, ventilation, and air conditioning systems has gained increasing attention. In this paper, data-driven FDD is defined as those that are built or trained from data via machine learning or multivariate statistical analysis methods. Following this definition, this paper reviews and summarizes the literature on data-driven FDD from three aspects: process, systems studied (including the systems being investigated, the faults being identified, and the associated data sources), and evaluation metrics. A data-driven FDD process is further divided into the following steps: data collection, data cleansing, data preprocessing, baseline establishment, fault detection, fault diagnostics, and potential fault prognostics. Literature reported data-driven methods used in each step of an FDD process are firstly discussed. Applications of data-driven FDD in various HVAC systems/components and commonly used data source for FDD development are reviewed secondly, followed by a summary of typical metrics for evaluating FDD methods. Finally, this literature review concludes that despite the promising performance reported in the literature, data-driven FDD methods still face many challenges, such as real-building deployment, performance evaluation and benchmarking, scalability and transferability, interpretability, cyber security and data privacy, user experience, etc. Addressing these challenges is critical for a broad real-building adoption of data-driven FDD.
Metrics
Details
- Title
- A review of data-driven fault detection and diagnostics for building HVAC systems
- Creators
- Zhelun Chen - Drexel University, Civil, Architectural, and Environmental EngineeringZheng O'Neill - Texas A&M UniversityJin Wen - Drexel University, Civil, Architectural, and Environmental EngineeringOjas Pradhan - Drexel Univ, Philadelphia, PA 19104 USATao Yang - Texas A&M Univ, College Stn, TX USAXing Lu - Texas A&M Univ, College Stn, TX USAGuanjing Lin - Lawrence Berkeley Natl Lab, Berkeley, CA USAShohei Miyata - The University of TokyoSeungjae Lee - University of TorontoChou Shen - University of TorontoRoberto Chiosa - Politecnico di TorinoMarco Savino Piscitelli - Politecnico di TorinoAlfonso Capozzoli - Politecnico di TorinoFranz Hengel - AEE Institute for Sustainable TechnologiesAlexander Kuehrer - AEE Inst Sustainable Technol, Gleisdorf, AustriaMarco Pritoni - Lawrence Berkeley National LaboratoryWei Liu - KTH Royal Inst Technol, Stockholm, SwedenJohn Clauss - SINTEF CommunityYimin Chen - Lawrence Berkeley Natl Lab, Berkeley, CA USATerry Herr - Intel (Brazil)
- Publication Details
- Applied energy, v 339, 121030
- Publisher
- Elsevier
- Number of pages
- 18
- Grant note
- DE-EE0008694; DE-AC0205CH11231 / U.S. Department of Energy; United States Department of Energy (DOE) DE-EE0009150; DE-EE0009153 / Building Technologies Office at the U.S. Department of Energy through the Emerging Technologies program 2050509 / NSF; National Science Foundation (NSF)
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Civil, Architectural, and Environmental Engineering
- Web of Science ID
- WOS:000965946800001
- Scopus ID
- 2-s2.0-85151031548
- Other Identifier
- 991021873511804721
UN Sustainable Development Goals (SDGs)
This publication has contributed to the advancement of the following goals:
Source: SDGs in the Output
InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
Highly Cited Paper
- Collaboration types
- Domestic collaboration
- International collaboration
- Web of Science research areas
- Energy & Fuels
- Engineering, Chemical