Comparison of two model based automated fault detection and diagnosis methods for centrifugal chillers

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Research has been ongoing during the last several years on developing robust automated fault detecting and diagnosing (FDD) methods applicable for process faults in chillers used in commercial buildings. These FDD methods involve using sensor data from available thermal, pressure and electrical measurements from commercial chillers to compute characteristic features (CF) which allow more robust and sensitive fault detection than using the basic sensor data itself. One of the proposed methods is based on the analytical redundancy approach using polynomial black-box multiple linear regression models for each CF that are identified from fault-free data in conjunction with a diagnosis table. The second method is based on a classification approach involving linear discriminant analysis to identify the classification models whereby both the detection and diagnosis can be done simultaneously. This paper describes the mathematical basis of both methods, illustrates how they are to be tuned using the same fault-free data set in conjunction with limited faulty data, and then compares their performance when applied to different fault severity levels. The relative advantages and disadvantages of each method are highlighted and future development needs are pointed out.

Original languageEnglish (US)
Title of host publication2008 Proceedings of the 2nd International Conference on Energy Sustainability, ES 2008
Pages577-588
Number of pages12
StatePublished - Oct 19 2009
Externally publishedYes
Event2008 2nd International Conference on Energy Sustainability, ES 2008 - Jacksonville, FL, United States
Duration: Aug 10 2008Aug 14 2008

Publication series

Name2008 Proceedings of the 2nd International Conference on Energy Sustainability, ES 2008
Volume1

Other

Other2008 2nd International Conference on Energy Sustainability, ES 2008
CountryUnited States
CityJacksonville, FL
Period8/10/088/14/08

Keywords

  • Centrifugal chillers
  • Fault detection and diagnosis
  • Model based automated fault detection methods

ASJC Scopus subject areas

  • Energy Engineering and Power Technology
  • Renewable Energy, Sustainability and the Environment
  • Mechanical Engineering

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