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Original Articles

A review of fault detection and diagnostics methods for building systems

& ORCID Icon
Pages 3-21 | Received 22 Sep 2016, Accepted 22 Mar 2017, Published online: 08 May 2017
 

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.

Nomenclature
AFFD=

= automated fault detection and diagnostics

AHU=

= air-handling unit

ANN=

= artificial neural network

AR=

= autoregressive

ARMA=

= autoregressive-moving–average

BPNN=

= back-propagation neural network

CUSUM=

= cumulative sum

GRNN=

= general regression neural network

HVAC=

= heating, ventilation, and air conditioning

HVAC&R=

= HVAC and refrigeration

LED=

= light-emitting diode

JAA=

= joint angle analysis

PCA=

= principal component analysis

PID=

= proportional integral and derivative

RNN=

= recurrent neural network

RTUs=

= rooftop units

SPC=

= statistical process control

SAX=

= symbolic aggregate approximation

SEER=

= seasonal energy efficiency ratio

SVM=

= support vector machine

VAV=

= variable-air volume

Acknowledgments

The authors thank Dr. Marina Sofos, Technology Development Manager, for her guidance and strong support of this work. At PNNL, the authors acknowledge Susan Ennor for her editorial support in preparing this article.

Funding

The authors acknowledge the Buildings Technologies Office of the U.S. Department of Energy's Office of Energy Efficiency and Renewable Energy (under Contract DE-AC05-76RL01830) for supporting this research and development effort.

Notes

1 Q-statistics is a test statistic output used in statistical hypothesis testing that provides better small-sample properties.

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