A novel model-based fault detection method for temperature sensor using fractal correlation dimension

Building and Environment

Volumn 46, Issue 4, 2011, Pages 970-979

  Yang, Xue Bin ^a   Jin, Xin Qiao ^a   Du, Zhi Min ^a   Zhu, Yong Hua ^a  

^a SHANGHAI JIAO TONG UNIVERSITY   (China)

Author keywords

Air handling unit; Fault detection; Fractal correlation dimension; Noise; Temperature sensor

Indexed keywords

AIR HANDLING UNITS; CORRELATION DIMENSIONS; DETECTION METHODS; EMBEDDING DIMENSIONS; FAULT DETECTION RATE; HYBRID FAULT DETECTION; LENGTH SCALE; MAIN PARAMETERS; MEASURED DATA; MEASURED SIGNALS; MODEL-BASED FAULT DETECTION; NOISE; NOISE CONDITIONS; NOVEL METHODS; PROCESS VARIABLES; SUPPLY AIR TEMPERATURE; TIME DELAY PARAMETERS;

FRACTAL DIMENSION; TEMPERATURE SENSORS;

FAULT DETECTION;

AIR TEMPERATURE; DETECTION METHOD; FAULT; FRACTAL ANALYSIS; NOISE; NUMERICAL MODEL;

EID: 78649955453     PISSN: 03601323     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.buildenv.2010.10.030     Document Type: Article

References (29)

1. Dexter A., Pakanen J. IEA annex 34. Demonstrating automated fault detection and diagnosis methods in real buildings. International Energy Agency 2001.
2. Du Z.M., Jin X.Q., Yang X.B. A robot fault diagnostic tool for flow rate sensors in air dampers and VAV terminals. Energy and Buildings 2009, 41(3):279-286.
3. Lee W.Y., House J.M., Kyong N.H. Subsystem level fault diagnosis of a building's air-handling unit using general regression neural networks. Applied Energy 2004, 77(2):153-170.
4. Norford L.K., Wright J.A., Buswell R.A., Luo D., Klaassen C.J., Suby A. Demonstration of fault detection and diagnosis methods for air-handling units (ASHRAE 1020-RP). HVAC and R Research 2002, 8(1):41-71.
5. Yiu J.C.M., Wang S. Multiple ARMAX modeling scheme for forecasting air conditioning system performance. Energy Conversion and Management 2007, 48(8):2276-2285.
6. Yoshida H., Kumar S., Morita Y. Online fault detection and diagnosis in VAV air handling unit by RARX modeling. Energy and Buildings 2001, 33(4):391-401.
7. Cui J., Wang S. A model-based online fault detection and diagnosis strategy for centrifugal chiller systems. International Journal of Thermal Sciences 2005, 44(10):986-999.
8. Katipamula S., Brambley M.R. Methods for fault detection, diagnostics, and prognostics for building systems - A review. Part I. HVAC And R Research 2005, 11(1):3-25.
9. Hyvärinen J., Kärki S. IEA annex 25. Building Optimization and fault diagnosis Source Book. International Energy Agency 1996.
10. Carling P., Haves P. Comparison of three fault detection methods based on field data of an air-handling unit. ASHRAE Transactions 2002, 108(1):904-921.
11. Reddy T.A., Niebur D., Andersen K.K., Pericolo P.P., Cabrera G. Evaluation of the suitability of different chiller performance models for on-line Training applied to automated fault detection and diagnosis (RP-1139). HVAC and R Research 2003, 9(4):385-414.
12. Jiang W., Reddy T.A. Reevaluation of the Gordon-Ng performance models for water-cooled chillers. ASHRAE Transactions 2003, 109(2):272-287.
13. Wang S., Wang J.B. Robust sensor fault diagnosis and validation in HVAC systems. Transactions of the Institute of Measurement and Control 2002, 24(3):231-262.
14. Wang S., Jiang Z. Valve fault detection and diagnosis based on CMAC neural networks. Energy and Buildings 2004, 36(6):599-610.
15. Hou Z., Lian Z., Yao Y., Yuan X. Data mining based sensor fault diagnosis and validation for building air conditioning system. Energy Conversion and Management 2006, 47(15-16):2479-2490.
16. Cho S.H., Yang H.C., Zaheer-Uddin M., Ahn B.C. Transient pattern analysis for fault detection and diagnosis of HVAC systems. Energy Conversion and Management 2005, 46(18-19):3103-3116.
17. Isermann R. Supervision, fault-detection and fault-diagnosis methods - An introduction. Control Engineering Practice 1997, 5(5):639-652.
18. Rossi T.M., Braun J.E. A statistical, rule-based fault detection and diagnostic method for vapor compression air conditioners. HVAC and R Research 1997, 3(1):19-37.
19. Theiler J. Efficient algorithm for estimating the correlation dimension from a set of discrete points. Physical Review A 1987, 36(9):4456-4462.
20. Grassberger P., Procaccia I. Characterization of strange attractors. Physical Review Letters 1983, 50(5):346-349.
21. Serinko R.J. Ergodic theorems arising in correlation dimension estimation. Journal of Statistical Physics 1996, 85(1-2):25-40.
22. Roy P.N.S., Ram A. A correlation integral approach to the study of 26 January 2001 Bhuj earthquake, Gujarat, India. Journal of Geodynamics 2006, 41(4):385-399.
23. Jin X.Q., Ren H.G., Xiao X.K. Prediction-based online optimal control of outdoor air of multi-zone VAV air conditioning systems. Energy and Buildings 2005, 37(9):939-944.
24. Lee S.H., Yik F.W.H. A study on the energy penalty of various air-side system faults in buildings. Energy and Buildings 2010, 42(1):2-10.
25. Isermann R. Model-based fault-detection and diagnosis - Status and applications. Annual Reviews in Control 2005, 29(1):71-85.
26. Wang S., Cui J. A robust fault detection and diagnosis strategy for centrifugal chillers. HVAC and R Research 2006, 12(3):407-428.
27. Isermann R. Fault-diagnosis systems: an introduction from fault detection to fault tolerance 2006, Springer-Verlag, Berlin, Heidelberg.
28. Lai Y.C., Lerner D. Effective scaling regime for computing the correlation dimension from chaotic time series. Physica D: Nonlinear Phenomena 1998, 115(1-2):1-18.
29. Kacimi S., Laurens S. The correlation dimension: a robust chaotic feature for classifying acoustic emission signals generated in construction materials. Journal of Applied Physics 2009, 106(2). Art. No. 024909.