Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

Case study results: fault detection in air-handling units in buildings

Case study results: fault detection in air-handling units in buildings The building sector of the U.S. currently consumes over 40% of the U.S. primary energy supply. This paper presents analytical methods embodied within useful software tools to quickly identify and evaluate selected building system faults that cause large building energy inefficiencies. These contributions are particularly unique in their treatment of models and the careful consideration of user interests in fault evaluation. As a first step to developing this general framework for fault detection, first-order faults such as simultaneous heating and cooling and imbalanced airflows within several large air-handling units were targeted. Savings of around $22,500 were predicted when the fault of simultaneous heating and cooling occurred over an entire month in an air handler. An example of the potential energy savings in a large hospital that has been monitored. Yearly savings of around $24,000 were predicted by correcting the operation of the circulation pump in an air-handler heat recovery loop. User testing and experiments show that embracing uncertainty within HVAC fault detection and evaluation is not only paramount to judicious fault inference but it is also central to gaining the trust and buy-in of system users who ultimately can apply fault detection information to actually fix and improve building operations. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Advances in Building Energy Research Taylor & Francis

Case study results: fault detection in air-handling units in buildings

Download PDF

Case study results: fault detection in air-handling units in buildings

Abstract

The building sector of the U.S. currently consumes over 40% of the U.S. primary energy supply. This paper presents analytical methods embodied within useful software tools to quickly identify and evaluate selected building system faults that cause large building energy inefficiencies. These contributions are particularly unique in their treatment of models and the careful consideration of user interests in fault evaluation. As a first step to developing this general framework for fault...
Loading next page...
 
/lp/taylor-francis/case-study-results-fault-detection-in-air-handling-units-in-buildings-Szs6OmwuA0
Publisher
Taylor & Francis
Copyright
© 2018 Informa UK Limited, trading as Taylor & Francis Group
ISSN
1756-2201
eISSN
1751-2549
DOI
10.1080/17512549.2018.1545143
Publisher site
See Article on Publisher Site

Abstract

The building sector of the U.S. currently consumes over 40% of the U.S. primary energy supply. This paper presents analytical methods embodied within useful software tools to quickly identify and evaluate selected building system faults that cause large building energy inefficiencies. These contributions are particularly unique in their treatment of models and the careful consideration of user interests in fault evaluation. As a first step to developing this general framework for fault detection, first-order faults such as simultaneous heating and cooling and imbalanced airflows within several large air-handling units were targeted. Savings of around $22,500 were predicted when the fault of simultaneous heating and cooling occurred over an entire month in an air handler. An example of the potential energy savings in a large hospital that has been monitored. Yearly savings of around $24,000 were predicted by correcting the operation of the circulation pump in an air-handler heat recovery loop. User testing and experiments show that embracing uncertainty within HVAC fault detection and evaluation is not only paramount to judicious fault inference but it is also central to gaining the trust and buy-in of system users who ultimately can apply fault detection information to actually fix and improve building operations.

Journal

Advances in Building Energy Research – Taylor & Francis

Published: Jul 2, 2020

Keywords: Fault detection; energy efficiency; heat recovery; sustainability; optimization

References

  • Fault detection for systems with multiple unknown modes and similar units and its application to HVAC
  • Data-driven fault detection and diagnosis for HVAC water chillers
  • Fault diagnosis in HVAC chillers using data-driven techniques
  • A smart lighting system for visual comfort and energy savings in industrial and domestic Use
  • Online support vector machine application for model based fault detection and isolation of HVAC system
  • Demonstrating automated fault detection and diagnosis methods in real buildings
  • Smart building creation in large scale HVAC environments through automated fault detection and diagnosis
  • Fault detection and diagnosis for buildings and HVAC systems using combined neural networks and subtractive clustering analysis
  • Intelligent infrastructure for energy efficiency
  • An expert rule set for fault detection in air-handling units/discussion
  • Methods for fault detection, diagnostics, and prognostics for building systems — A review, part II
  • Methods for fault detection diagnostics and prognostics for building systems — A review part I
  • A review of fault detection and diagnostics methods for building systems
  • An overview of artificial intelligence-based methods for building energy systems
  • Fault tolerant control strategies for a high-rise building hot water heating system
  • Demonstration of fault detection and diagnosis methods for Air-handling units demonstration of fault detection and diagnosis methods for Air-handling units
  • Model-based real-time whole building energy performance monitoring and diagnostics
  • An online model-based fault diagnosis scheme for HVAC systems
  • Advanced detection of HVAC faults using unsupervised SVM novelty detection and Gaussian process models
  • Comparative study on machine learning for urban building energy analysis
  • Automated fault detection And diagnosis Of HVAC subsystems using statistical machine learning
  • A review of fault detection and diagnosis methodologies on air-handling units