A review of fault detection and diagnostics methods for building systems

doi:10.1080/23744731.2017.1318008

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A review of fault detection and diagnostics methods for building systems

Journal Article•DOI•

A review of fault detection and diagnostics methods for building systems

Woohyun Kim¹, Srinivas Katipamula¹•Institutions (1)

Pacific Northwest National Laboratory¹

02 Jan 2018-Science and Technology for the Built Environment (Taylor & Francis)-Vol. 24, Iss: 1, pp 3-21

TL;DR: In this article, the authors provide a summary of automated fault detection and diagnostics studies published since 2004 that are relevant to the commercial buildings sector and provide a guideline for selecting an appropriate automated fault detector and diagnostic method.

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Abstract: The current article provides a summary of automated fault detection and diagnostics studies published since 2004 that are relevant to the commercial buildings sector. The review updates a previous review conducted in 2004 and published in 2005, and it categorizes automated fault detection and diagnostics methods into three groups. The examples of automated fault detection and diagnostics in the primary category are selectively reviewed to identify various methods that are suitable for building systems and to understand the strengths and weaknesses of the methods. The distribution of studies based on each automated fault detection and diagnostics method and heating, ventilation, and air-conditioning system is also described. Researchers and industries can use the current article as a guideline for selecting an appropriate automated fault detection and diagnostics method.

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Energy and buildings

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Akeem Richards

05 Mar 2012

317 citations

Journal Article•DOI•

Artificial intelligence-based fault detection and diagnosis methods for building energy systems: Advantages, challenges and the future

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Yang Zhao¹, Tingting Li¹, Xuejun Zhang¹, Chaobo Zhang¹•Institutions (1)

Zhejiang University¹

01 Jul 2019-Renewable & Sustainable Energy Reviews

TL;DR: It is concluded that new artificial intelligence-based methodologies are needed to be able to combine the advantages of both kinds of methods in the future.

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Abstract: Artificial intelligence has showed powerful capacity in detecting and diagnosing faults of building energy systems. This paper aims at making a comprehensive literature review of artificial intelligence-based fault detection and diagnosis (FDD) methods for building energy systems in the past twenty years from 1998 to 2018, summarizing the strengths and shortcomings of the existing artificial intelligence-based methods, and revealing the most important research tasks in the future. Challenges in developing FDD methods for building energy systems are discussed firstly. Then, a comprehensive literature review is made. All methods are classified into two categories, i.e. data driven-based and knowledge driven-based. The data driven-based methods are abundant, including the classification-based, unsupervised learning-based and regression-based. They showed powerful capacity in learning patterns from training data. But, they need a large amount of training data, and have problems in reliability and robustness. The knowledge driven-based methods show powerful capacity in simulating the diagnostic thinking of experts. But, they rely on expert knowledge heavily. It is concluded that new artificial intelligence-based methodologies are needed to be able to combine the advantages of both kinds of methods in the future.

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280 citations

Journal Article•DOI•

Smart energy systems for sustainable smart cities: Current developments, trends and future directions

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Edward O’Dwyer¹, Indranil Pan¹, Indranil Pan², Salvador Acha¹, Nilay Shah¹ - Show less +1 more•Institutions (2)

Imperial College London¹, The Turing Institute²

01 Mar 2019-Applied Energy

TL;DR: This review looks at the current developments in the smart energy sector, focussing on techniques in the main application areas along with relevant implemented examples, while highlighting some of the key challenges currently faced and outlining future pathways for the sector.

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222 citations

Journal Article•DOI•

A review of machine learning in building load prediction

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Liang Zhang¹, Jin Wen², Yanfei Li¹, Jianli Chen³, Yunyang Ye⁴, Yangyang Fu⁵, William Livingood¹ - Show less +3 more•Institutions (5)

National Renewable Energy Laboratory¹, Drexel University², University of Utah³, Pacific Northwest National Laboratory⁴, Texas A&M University⁵

01 Mar 2021-Applied Energy

TL;DR: This paper reviews the application of machine learning techniques in building load prediction under the organization and logic of the machine learning, which is to perform tasks T using Performance measure P and based on learning from Experience E.

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197 citations

Journal Article•DOI•

A review of smart building sensing system for better indoor environment control

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Bing Dong¹, Vishnu Prakash², Fan Feng³, Zheng O'Neill²•Institutions (3)

Syracuse University¹, University of Texas at San Antonio², University of Alabama³

15 Sep 2019-Energy and Buildings

TL;DR: A systemic review of how indoor sensors influence in managing optimal energy saving, thermal comfort, visual comfort, and indoor air quality in the built environment is provided.

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171 citations

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Journal Article•DOI•

Methods for Fault Detection, Diagnostics and Prognostics for Building Systems - A Review Part II

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Srinivas Katipamula, Michael R. Brambley¹•Institutions (1)

Pacific Northwest National Laboratory¹

01 Apr 2005-Hvac&r Research

TL;DR: In this article, the second part of a two-part review of methods for automated fault detection and diagnostics (FDD) and prognostics whose intent is to increase awareness of the HVAC&R research and development community is presented.

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Abstract: This paper is the second of a two-part review of methods for automated fault detection and diagnostics (FDD) and prognostics whose intent is to increase awareness of the HVAC&R research and development community to the body of FDD and prognostics developments in other fields as well as advancements in the field of HVAC&R. The first part of the review focused on generic FDD and prognostics, provided a framework for categorizing methods, described them, and identified their primary strengths and weaknesses (Katipamula and Brambley 2005). In this paper we address research and applications specific to the fields of HVAC&R, provide a brief discussion on the current state of diagnostics in buildings, and discuss the future of automated diagnostics in buildings.

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976 citations

Energy and buildings

[...]

Akeem Richards

05 Mar 2012

317 citations

"A review of fault detection and dia..." refers background or methods in this paper

...Li and Braun (2007a) presented improvements to the original statistical rule-based AFDD algorithm for RTUs that have a fixed-speed compressor and a fixed orifice....
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...Li and Braun (2007b) present an integrated AFDD algorithm based on a combination of gray box and black box statistical polynomial regression models....
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...…faults when there are multiple simultaneous faults, numerous studies have combined black box models with gray box or qualitative models (Fontugne et al. 2013; Bynum et al. 2012; Li and Braun 2007b; Lin and Claridge 2015; Wang and Cui 2006; Wang et al. 2013; Yang et al. 2013; Zhao et al. 2014)....
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...Some of these studies are discussed below (Armstrong et al. 2006; Hjortland 2014; Li 2012; Li and Braun 2007a)....
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...Some recent studies (Han et al. 2011a; Li and Braun 2007) have also been able to detect multiple simultaneous faults....
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Journal Article•DOI•

Building operation and energy performance: Monitoring, analysis and optimisation toolkit

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Andrea Costa¹, Marcus M. Keane¹, J. Ignacio Torrens¹, Edward Corry¹•Institutions (1)

National University of Ireland¹

01 Jan 2013-Applied Energy

TL;DR: In this paper, the authors present an integrated toolkit designed to assist energy managers at different stages of their activity relating to systematic energy management in buildings, focusing on the applicability of the integrated tool-kit in the building industry.

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309 citations

"A review of fault detection and dia..." refers background in this paper

...Research on whole building diagnostics has been a subject of interest over the past decade (Bynum et al. 2012; Capozzoli et al. 2015; Costa et al. 2013; Lin and Claridge 2015; Liu et al. 2010; Miller et al. 2015; Narayanaswamy et al. 2014; Seem 2007)....
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Journal Article•DOI•

A rule-based fault detection method for air handling units

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J Schein¹, Steven T. Bushby¹, Natascha S. Castro¹, John M. House•Institutions (1)

National Institute of Standards and Technology¹

01 Dec 2006-Energy and Buildings

TL;DR: APAR as mentioned in this paper is a fault detection tool that uses a set of expert rules derived from mass and energy balances to detect faults in air handling units (AHUs). Control signals are used to determine the mode of operation of the AHU.

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254 citations

"A review of fault detection and dia..." refers background in this paper

...Schein et al. (2006) extended an earlier study that proposed a rule set for AHUs by House et al. (2001)....
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...Expert systems are widely used for AFDD of building systems because of their simplicity to develop and apply in real systems (Bruton et al. 2014; Cho et al. 2005; Choinière 2008; Schein and Bushby 2006; Schein et al. 2006; Song et al. 2008; Yang et al. 2008)....
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Journal Article•DOI•

Fault detection and diagnosis for buildings and HVAC systems using combined neural networks and subtractive clustering analysis

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Zhimin Du¹, Bo Fan¹, Xinqiao Jin¹, Jinlei Chi¹•Institutions (1)

Shanghai Jiao Tong University¹

01 Mar 2014-Building and Environment

TL;DR: In this paper, a robust diagnostics tool is presented to improve the energy efficiency and thermal comfort of buildings through removing various faults, such as sensor biases, drifting biases and complete failure of the sensors and chilled water valve faults.

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247 citations

"A review of fault detection and dia..." refers methods in this paper

...A number of researchers have used an ANN black box model for AFDD (Du et al. 2014; Fan et al. 2010; He et al. 2011, 2012; Hou et al. 2006; Jones 2015; Kim et al. 2008; Mavromatidis et al. 2013; Rueda et al. 2005; Yunwono et al. 2015; Zhu et al. 2012)....
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