Tolerant control for multiple faults of sensors in VAV systems

Energy Conversion and Management

Volumn 48, Issue 3, 2007, Pages 764-777

  Du, Zhimin ^a   Jin, Xinqiao ^a  

^a SHANGHAI JIAO TONG UNIVERSITY   (China)

Author keywords

Detection; Isolation; Multiple faults; Reconstruction; Sensor; Tolerant control; Variable air volume

Indexed keywords

ESTIMATION; MATHEMATICAL MODELS; PRINCIPAL COMPONENT ANALYSIS; SENSORS;

FAULTY OPERATIONS; JOINT ANGLE PLOTS; MULTIPLE FAULTS; VARIABLE AIR VOLUME (VAV) SYSTEMS;

ELECTRIC FAULT LOCATION;

EID: 33846318173     PISSN: 01968904     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.enconman.2006.09.007     Document Type: Article

References (29)

1. Usoro P.B., Schick I.C., and Negahdaripour S. An innovation-based methodology for HVAC system fault detection. J Dyn Syst Meas Control 107 (1985) 284-285
2. Anderson D., Graves L., Reinert W., Kreider J.F., Dow J., and Wubbena H. A quasi-real-time expert system for commercial building HVAC diagnostics. ASHRAE Trans 95 2 (1989)
3. Hyvarnen J., et al. IEA ANNEX 25, building optimization and fault diagnosis source book (1995), International Energy Agency, Paris
4. Dexter AL, Pakanen J. Demonstrating automated fault detection and diagnosis methods in real buildings. VTT Building Technology, Finland (ISBN 951-38-5726-3), ANNEX 34 Final Report, 2001, IEA.
5. Piette M.A., Kinney S.K., and Philip H. Analysis of an information monitoring and diagnostic system to improve building operation. Energy Build 33 8 (2001) 783-791
6. Comstock MC, Braun JE. Development of analysis tools for the evaluation of fault detection and diagnostics in chillers. Report #HL99-20. Purdue University, Ray W. Herrick Laboratories, West Lafayette, IN, 1999.
7. Peitsman H., and Bakker V.E. Application of black-box models to HVAC systems for fault detection. ASHRAE Trans 102 2 (1996) 628-640
8. Rossi T.M., and Braun J.E. A statistical, rule-based fault detection and diagnostic method for vapor compression air conditioners. Int J Heat Ventilat Air Condition Refrig Res 3 1 (1997) 19-37
9. Yoshida H., Iwami T., Yuzawa H., and Suzuki M. Typical faults of air-conditioning systems and fault detection by ARX model and extended Kalman filter. ASHRAE Trans 102 1 (1996) 557-564
10. Lee W.Y., Park C., and Kelly G.E. Fault detection in an air-handling unit using residual and recursive parameter identification methods. ASHRAE Trans 102 2 (1996) 528-539
11. Ngo D., and Dexter A.L. A robust model-based approach to diagnosing faults in air-handling units. ASHRAE Trans 105 1 (1999) 1078-1086
12. House J.M., Vaezi-Nejad H., and Whitcomb J.M. An expert rules set for fault detection in air handling units/discussion. ASHRAE Trans 107 1 (2001) 858-871
13. Stylianou M., and Nikanour D. Performance monitoring, fault detection, and diagnosis of reciprocating chillers. ASHRAE Trans 102 1 (1996) 615-627
14. Wang S.W., and Wang J.B. Robust sensor fault diagnosis and validation in HVAC systems. Trans Inst Meas Control 24 3 (2002) 231-262
15. Howell J., and Maddison E.J. Fault detection in HVAC plants based on constraint suspension. Build Serv Eng Res Technol 16 4 (1995) 207-213
16. Haves P., Salsbury T.I., and Wright J.A. Condition monitoring in HVAC subsystem using first principles models. ASHRAE Trans 102 1 (1996) 519-527
17. Tzafestas S. Second generation expert systems: requirements, architectures and prospects. In: IFAC/IMACS symposium on fault detection, supervision and safety for technical process, Baden-Baden, 1991.
18. Hemmelblau DM, Use of artificial neural networks to monitor faults and for troubleshooting in the process industries. In: IFAC symposium on on-line fault detection and supervision in the chemical process industries, Newark, 1992.
19. Dexter A.L., and Ngo D. Fault diagnosis in HVAC systems: a multi-step fuzzy model-based approach. Int J HVAC&R Res 7 1 (2001) 83-102
20. Lee W.Y., House J.M., and Shin D.R. Fault diagnosis and temperature sensor recovery for an air-handling unit. ASHRAE Trans 103 1 (1997) 621-633
21. Wang S.W., and Chen Y.M. Fault-tolerant control for outdoor ventilation air flow rate in building based on neural network. Build Environ 37 7 (2002) 691-704
22. Wang S.W., and Xiao F. AHU sensor fault diagnosis using principal component analysis method. Energy Build 36 (2004) 147-160
23. Wang S.W., and Cui J.T. Sensor-fault detection, diagnosis and estimation for centrifugal chiller systems using principal-component analysis method. Appl Energy 82 (2005) 197-213
24. Jackson J.E., and Mudholkar G.S. Control procedures for residuals associated with principal components analysis. Technometrics 21 (1979) 341-349
25. Edward J. User's guide to principal components (1991), Wiley
26. Jolliffe I.T. Principal component analysis (1986), Springer-Verlag, NewYork
27. Dunia R., and Joe Qin S. Joint diagnosis of process and sensor faults using principal component analysis. Control Eng Practice 6 (1998) 457-469
28. Yoon S., and MacGregor J.F. Fault diagnosis with multivariate statistic models. Part I: Using steady state fault signatures. J Process Control 11 1 (2001) 387-400
29. Jin X.Q. Study on simulation of VAV air-conditioning system and online optimal control (1999), Shanghai Jiaotong University, Shanghai