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

The Society of Air-Conditioning and Refrigerating Engineers of Korea (대한설비공학회)
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The Fault Detection of an Air-Conditioning System by Using a Residual Input RBF Neural Network

잔차입력 RBF 신경망을 사용한 냉방기 고장검출 알고리즘

  • Han, Do-Young (Department of Mechanical and Automotive engineering, Kookmin University) ;
  • Ryoo, Byoung-Jin (Graduate School of Mechanical Engineering, Kookmin University)
  • 한도영 (국민대학교 기계 자동차공학부) ;
  • 류병진 (국민대학교 기계공학과 대학원)
  • Published : 2005.08.01
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Abstract

Two different types of algorithms were developed and applied to detect the partial faults of a multi-type air conditioning system. Partial faults include the compressor valve leakage, the refrigerant pipe partial blockage, the condenser fouling, and the evaporator fouling. The first algorithm was developed by using mathematical models and parity relations, and the second algorithm was developed by using mathematical models and a RBF neural network. Test results showed that the second algorithm was better than the first algorithm in detecting various partial faults of the system. Therefore, the algorithm developed by using mathematical models and a RBF neural network may be used for the detection of partial faults of an air-conditioning system.

Keywords

References

  1. Breuker, M. S. and Braun, J E., 1998, Common faults and their impacts for rooftop air conditioners, HVAC&R Research, Vol. 4, No. 3, pp.303-318 https://doi.org/10.1080/10789669.1998.10391406
  2. Breuker, M. S. and Braun, J. E, 1998, Evaluating the performance of a fault detection and diagnostic system for vapor compression equipment, ASHRAE HVAC&R Research, Vol. 4, No.4, pp.401-425 https://doi.org/10.1080/10789669.1998.10391412
  3. Riemer, P. L., Mitchell, J. W. and Beckman, W. A., 2002, The use of series analysis in fault detection and diagnosis methodologies, ASHRAE Transactions. 2002, V.108, Pt. 2
  4. Peitsman, H. C. and Bakker, V. E., 1996, Application of black-box models to HVAC systems for fault detection, ASHRAE Transactions, pp. 628-640
  5. Peitsman, H. C. and Soethout, L. L., 1997, ARX models and real-time model-based diagnosis, ASHHAE Transactions, pp.657-671
  6. McIntosh, I. B. D., Mitchell, J. W. and Beckman, W. A., 2000, Fault detection and diagnosis in chillers, ASHRAE Transactions 2000, V.106, Pt. 2
  7. Frank, D. and Pletta, J. B., 1992, Neural network sensor fusion for security application, Intelligent Engineering Systems through Artificial Neural Networks, Vol. 2, pp.745-750
  8. Ch'ng, C. G. and Yak, A. S., 1998, Neural networks for process diagnosis, ICARCY, pp. 494-498
  9. Han, D. and Lee, H., 2002, Partial fault detection of air-conditioning system by neural network algorithm using data preprocessing method, Korean Journal of the SAREK Vol. 14, No.7, pp.560-566
  10. Han, D. and Hwang, J, 2003, The partial fault detection of an air-conditioning system by the neural network algorithm using normalized input data, Korean Journal of the Air-Conditioning and Refrigeration Engineering, Vol. 15, No.3, pp.159-165
  11. Han, D. and Joo, M., 2002, Fault detection & diagnosis of an air handling unit based on rule bases, Korean Journal of the Air Conditioning and Refrigeration Engineering, Vol. 14, No.7, pp.552-559
  12. Han, D. and Kim, J., 2004, An experimental study on the rule based fault detection and diagnosis system for a constant air volume air handling unit, Korean Journal of the Air-Conditioning and Refrigeration Engineering, Vol. 16, No.9, pp.872-880
  13. Han, D. and Ryoo, B., 2003, Partial fault diagnosis of the air-conditioning system by using curve fitting model and neural network algorithm, Proceedings of the SAREK, pp.577-582
  14. Han, D. and Ryoo, B., 2004, Fault detection and diagnosis of the air-conditioning system by using a fuzzy algorithm and a RBF neural network, Proceedings of the SAREK, pp. 795-800