DOI QR코드

DOI QR Code

Validation of PCA Method for Failure Detection of Repeater of Measuring Sensor in Chiller

냉동기 계측센서의 중계기 고장 검출을 위한 주성분분석의 유효성 검증

  • Received : 2015.01.08
  • Accepted : 2015.03.08
  • Published : 2015.03.30

Abstract

Building energy management system(BEMS) in order to save the energy used in the operation stage of buildings has been actively introduced recently and it is recommended to apply BEMS to various certification system, but, there is a real situation such as low utilization level of comparison of energy savings and monitoring function oriented simple statistical processing. In this study, by using the principal component analysis(PCA) method that is able to analyze whether failure in repeater of measuring sensor for the chiller measurement data in the actual building where BEMS is operating, failures that exist in BEMS data were detected. PCA method is the most widely used method of adjusting the data and could detect a fault and improve the performance of diagnosis of low-dimensional description of data generated by the PCA method using multivariate statistics such as the Hotelling $T^2$ statistics and Q-statistics to monitor the industrial system. It is found that because the end of operation time before replacing the sensor and Q-statistics beyond Q-Thresholds has been appeared to the same sample position, the failure of the sensor can be detected by PCA method using the stored BEMS data.

Keywords

Acknowledgement

Supported by : 한남대학교

References

  1. 에너지관리공단(2013), BEMS 운영현황 분석방법론 개발에 관한 연구.
  2. Xiao, F., Wang, S.W. & Zhang, J.P. (2006). A Diagnostic Tool for Online Sensor Health Monitoring in Air-Conditioning Systems. Automation in Construction, 15, 489-503. https://doi.org/10.1016/j.autcon.2005.06.001
  3. Usoro, P. B., Schick, I. C. & Negahdaripour, S. (1985). HVAC System Fault Detection and Diagnosis. In American Control Conference, IEEE, 606-612.
  4. Lee, W.Y., House, J.M. & Shin, D.R. (1997). Fault diagnosis and temperature sensor recovery for an air-handling unit, ASHRAE Transactions 103(1), 621-633.
  5. Wang, S.W. & Wang, J.B. (2002). Automatic sensor evaluation in BMS commissioning of building refrigeration systems, Automation in construction 11.1, 59-73. https://doi.org/10.1016/S0926-5805(01)00050-4
  6. Wang, S,W. & Chen, Y. (2004). Sensor validation and reconstruction for building central chilling systems based on principal component analysis. Energy Conversion and management, 45(5), 673-695. https://doi.org/10.1016/S0196-8904(03)00180-8
  7. Wang, S,W. & Xiao, F. (2006). Sensor Fault Detection and Deiagnosis of Air-Handling Units Using a Condition-Based Adaptive Statistical Method, HVAC&R Research,12(1), 127-150. https://doi.org/10.1080/10789669.2006.10391171
  8. Wang, S.W. & Cui, J.T. (2006). A Robust Fault Detection and Diagnosis Strategy for Centrifugal Chillers, HVAC&R Research, 12(3), 407-428. https://doi.org/10.1080/10789669.2006.10391187
  9. 김희철 & 신현철. (2008). 지수가중이동평균 관리도를 이용한 소프트웨어 고장시간 비교분석에 관한 연구, 정보.보안논문지, 8(3), 33-39.
  10. Trevor Bailey et al., (2011). Automated continuous commissioning of commercial buildings, ESTCP project SI-0929.
  11. 김종결 외, (2010). 미세변동 감지를 위한 공정관리도의 연구 동향 분석. 대한산업공학회/한국경영과학회 춘계공동학술대회. 834-842.
  12. J. Edward Jackson. (1991). A User's Guide To Principal Components, Wiley Series in Probability and Statistics, 36-38.
  13. M. Ahmed et al., (2012). Fault Detection and Diagnosis using Principal Component Analysis of Vibration Data from a Reciprocating Compressor, 18th International Conference On Automation And Computing (ICAC).