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An OOM-KBES approach for fault detection and diagnosis

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Methodology and Tools in Knowledge-Based Systems (IEA/AIE 1998)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 1415))

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Abstract

This paper presents an integrated approach to the intelligent building research: using both the object-oriented modeling (OOM) and knowledge-based expert-system (KBES) methodologies and technologies for fault detection and diagnosis (FDD) of building HVAC systems. The approach consists of five basic steps: 1) establish a model simulating the behavior of the target system using object-oriented design methodologies; 2) identify all types of faults in the target system, extract rules for each process to build the knowledge bases; 3) integrate the knowledge bases into system model to allow the system perform FDD task on itself; 4) build an on-line monitoring system to collect all real-time setpoint data; and 5) make inference against the knowledge bases based on real time data and generate reports.

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References

  1. W. Y. Lee, J. M. House, C. Park, G. E. Kelly, “Fault Diagnosis of an Air-Handling Unit Using Artificial Neural Networks,” ASHRAE Transactions, Vol. 102 (1996)

    Google Scholar 

  2. http://www.ecbcs.org/annex25.htmi, “Real Time Simulation of HEVC Systems for Building Optimization, Fault Detection and Diagnosis,” IEA, (1997)

    Google Scholar 

  3. M. Stylianou and D. Nikanpour, “Performance Monitoring, Fault Detection, and Diagnosis of Reciprocating Chillers,” ASHRAE Transactions, Vol. 102 (1996)

    Google Scholar 

  4. H. Yoshida, et. al., “Typical Fault of Air-Conditioning Systems and Fault Detection by ARX Model and Extended Kalman Filter,” ASHRAE Transactions, Vol. 102 (1996)

    Google Scholar 

  5. H. Peitsman and V. E. Bakker, “Application of Black-box Models to HVAC Systems for Fault Detection,”, ASHRAE Transactions, Vol. 102 (1996)

    Google Scholar 

  6. A. L Dexter and M. Benouarets, “A Generic Approach to Identifying Faults in HVAC Plants,” ASHRAE Transactions, Vol. 102 (1996)

    Google Scholar 

  7. Charles C. Culp, The Impact of AI Technology within the HVAC Industry, ASHRAE Journal, Volume 32 Number 12 (1990)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Electrical & Computer Engineering and Computer Science, University of Cincinnati, 45221-0030, Cincinnati, Ohio, USA

    Yunfeng Xiao & Chia Y. Han

Authors
  1. Yunfeng Xiao
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  2. Chia Y. Han
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Editor information

José Mira Angel Pasqual del Pobil Moonis Ali

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© 1998 Springer-Verlag

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Xiao, Y., Han, C.Y. (1998). An OOM-KBES approach for fault detection and diagnosis. In: Mira, J., del Pobil, A.P., Ali, M. (eds) Methodology and Tools in Knowledge-Based Systems. IEA/AIE 1998. Lecture Notes in Computer Science, vol 1415. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-64582-9_816

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  • DOI: https://doi.org/10.1007/3-540-64582-9_816

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-64582-5

  • Online ISBN: 978-3-540-69348-2

  • eBook Packages: Springer Book Archive

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