Korean Journal of Air-Conditioning and Refrigeration Engineering (설비공학논문집)

The Society of Air-Conditioning and Refrigerating Engineers of Korea (대한설비공학회)
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Fault Detection and Diagnosis Simulation for CAV AHU System

정풍량 공조시스템의 고장검출 및 진단 시뮬레이션

  • Han, Dong-Won (Graduate School of Mechanical Engineering, Korea University) ;
  • Chang, Young-Soo (Department of Advanced Fermentation Fusion Science and Technology, Kookmin University) ;
  • Kim, Seo-Young (Korea Institute of Science and Technology) ;
  • Kim, Yong-Chan (Department of Mechanical Engineering, Korea University)
  • Received : 2010.05.25
  • Published : 2010.10.10
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Abstract

In this study, FDD algorithm was developed using the normalized distance method and general pattern classifier method that can be applied to constant air volume air handling unit(CAV AHU) system. The simulation model using TRNSYS and EES was developed in order to obtain characteristic data of CAV AHU system under the normal and the faulty operation. Sensitivity analysis of fault detection was carried out with respect to fault progress. When differential pressure of mixed air filter increased by more than about 105 pascal, FDD algorithm was able to detect the fault. The return air temperature is very important measurement parameter controlling cooling capacity. Therefore, it is important to detect measurement error of the return air temperature. Measurement error of the return air temperature sensor can be detected at below $1.2^{\circ}C$ by FDD algorithm. FDD algorithm developed in this study was found to indicate each failure modes accurately.

Keywords

References

  1. Katipamula, S. and Brambley, M. R., 2005, Methods for fault detection, diagnostics, and prognostics for building systems-a review, part I, HVAC&R Research, Vol. 11, No. 1, pp. 3-25. https://doi.org/10.1080/10789669.2005.10391123
  2. Han, D. and Ha, S., 2000, A study on the fault detection and diagnosis method for the air conditioner, Proceedings of the SAREK, pp. 1408-1412.
  3. Kim, M. and Kim, M., 2005, Studies on the performance variation of a variable speed vapor compression under fault and its detection and diagnosis, Korean Journal of Air-Conditioning and Refrigeration Engineering, Vol. 17, No. 1, pp. 47-55.
  4. Lee, W. Y. and Kyong, N. H., 2001, Fault detection and diagnosis for an air-handling unit using artificial neural networks, Korean Journal of Air-Conditioning and Refrigeration Engineering, Vol. 13, No. 12, pp. 1288-1296.
  5. Peitsman, H. C. and Bakker, V. E., 1996, Application of black-box models to HVAC system for fault detection, ASHRAE Transactions, Vol. 102, No. 1, pp. 628-640.
  6. Peitsman, H. C. and Soethout, L. L., 1997, ARX models and real-time model-based diagnosis, ASHRAE Transactions, Vol. 103, No. 1, pp. 657-671.
  7. Han, C. Y., Xiao, Y. and Braun, C. J., 1999, Fault detection and diagnosis of HVAC systems, ASHRAE Transactions, Vol. 105, No. 1, pp. 568-578.
  8. House, J. M., Vaezi-Nejad, H. and Whitecomb, J. M., 2001, An expert rule set for fault detection in air-handling units, ASHRAE Transactions, Vol. 107, No. 1, pp. 858-871.
  9. Schein, J., Bushby, S. T., Castro, N. S. and House, J. M., 2006, A rule-based fault detection method for air handling units, Energy and Buildings, Vol. 38, pp. 1485-1492. https://doi.org/10.1016/j.enbuild.2006.04.014
  10. Yang, H., Cho, S., Tae, C. S. and Zaheeruddin, M., 2008, Sequential rule based algorithms for temperature sensor fault detection in air handling unit, Energy Conversion and Management, Vol. 29, pp. 2291-2306.
  11. SEL(Solar Energy Lab.), Trnsys 16 User's Manual, University of Wisconsin-Madison, Madison, WI, U. S. A.
  12. F-Chart Software, EES Manual, 2001, Version 6.20.
  13. Seok, H. T. and Kim, K. W., 2001, Thermal performance evaluation of design parameters and development of load prediction equations of office buildings, Korean Journal of Air-Conditioning and Refrigeration Engineering, Vol. 13, No. 9, pp. 914-921.
  14. Clark, D. R. and May, Jr. W. B., 1985, HVAC SIM+ building systems and equipment simulation program-user guide, NBSIR 85-3243, NIST.
  15. Han, D. W. and Chang, Y. S., 2008, Development of a fault detection and diagnosis algorithm using fault mode simulation for a centrifugal chiller, Korean Journal of Air-Conditioning and Refrigeration Engineering, Vol. 20, No. 10, pp. 669-678.
  16. Rossi, T. M. and Braun, J. E., 1997, A statistical, rule-based fault detection and diagnostic method for vapor compression air conditioner, Int. J. of HVAC&R Research, Vol. 3, No. 1, pp. 19-37. https://doi.org/10.1080/10789669.1997.10391359