대한기계학회논문집B (Transactions of the Korean Society of Mechanical Engineers B)

  • 제29권11호
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  • Pages.1220-1228
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  • 2005
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  • 1226-4881(pISSN)
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  • 2288-5324(eISSN)
대한기계학회 (The Korean Society of Mechanical Engineers)
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퍼지 알고리즘을 이용한 시스템 멀티 에어컨의 고장진단 알고리즘 개발

Fuzzy Algorithm for FDD Technique Development of System Multi-Air Conditioner

  • 최창식 (성균관대학교 대학원 기계공학과) ;
  • 태상진 (성균관대학교 대학원 기계공학과) ;
  • 김훈모 (성균관대학교 기계공학부) ;
  • 조금남 (성균관대학교 기계공학부) ;
  • 문제명 (삼성전자 시스템가전사업부) ;
  • 김종엽 (삼성전자 시스템가전사업부) ;
  • 권형진 (삼성전자 시스템가전사업부)
  • 발행 : 2005.11.01
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초록

Fault detection and diagnostic (FDD) systems have the potential to reduce equipment downtime, service costs, and utility costs. In this study, model based algorithm and fuzzy algorithm were used to detect and diagnose various fault at System multi-air conditioner. various fault include the Refrigerant Low charging, Fouling of Indoor Heat Exchanger, Fouling of Outdoor Heat Exchanger A experimental verification was conducted in the 6HP System multi-air conditioner on an 8-floor building. Test results showed diagnosis result about 78 $\~$ 90$\%$ for given faults. This Study lays the foundation fur future work on develope the real-time fault detection and diagnosis system for the System multi-air conditioner.

키워드

참고문헌

  1. Rossi, T. M. and Braun, J. E., 1997, 'A Statistical, Rule-Based Fault Detection and Diagnostic Method for Vapor Compression Air Conditioners,' International Journal of Heating, Ventilating, Air-Conditioning and Refrigerating Research, Vol. 3, No. 1, pp. 19-37 https://doi.org/10.1080/10789669.1997.10391359
  2. Breuker, M. S. and Braun, J. E., 1998, 'Common Fault and Their Impacts for Rooftop Air Conditioners,' ASHRAE HVAC&R Research, Vol. 4, No. 3 https://doi.org/10.1080/10789669.1998.10391388
  3. Han, D. Y. and Yoon, T. H., 2000, 'Partial Fault Response of Multi-Type Air-Conditioner,' Proceedings of the SAREK, pp. 319-323
  4. Han, D. Y. and Lee, H., 2002, 'Partial Fault Detection of Air-Conditioning System by Neural Network Algorithm Using Data Preprocessing Method,' Korean Journal of Air-conditioning and Refrigeration, Vol. 14, No. 7, pp. 560-566
  5. Han, D. Y. and Lee, H., 2002, 'Partial Fault Detection of Air-Conditioning System by Using the Model-Based Method with Data Preprocessing,' Proceedings of the SAREK, pp. 295-300
  6. Han, D. Y. and Hwang, J. U., 2003, 'The Partial Fault Detection of an Air-Conditioning System by the Neural Network Algorithm Using Normalized Input Data,' Koran Journal of Air-conditioning and Refrigeration, Vol. 15, No. 3, pp. 159-165
  7. Glass, A. S., Gruber, P., Ross, M. and Todtli, J., 1995, 'Qualitalitative Model-Based Fault Detection in Air-Handling Units,' IEEE Control Syst. Mag., Vol. 15, No. 4, pp. 11-22 https://doi.org/10.1109/37.408465
  8. http://www.fuzzytech.com
  9. Dexter, A. L., 1993, 'Fault Detection in Air-Conditioning System Using Fuzzy Models,' IEEE Colloquium Two Decades of Fuzzy Control Part 2, Digest No. 1993/118
  10. Dexter, A. L. and Benouarets, M., 1997, 'Model-Based Fault Diagnosis Using Fuzzy Matching,' IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS-PART A: SYSTEMS AND HUMANS, Vol. 27, No. 5 https://doi.org/10.1109/3468.618266