An isolation enhanced PCA method with expert-based multivariate decoupling for sensor FDD in air-conditioning systems

Applied Thermal Engineering

Volumn 29, Issue 4, 2009, Pages 712-722

  Xiao, Fu ^a   Wang, Shengwei ^a   Xu, Xinhua ^b   Ge, Gaoming ^a  

^a HONG KONG POLYTECHNIC UNIVERSITY   (Hong Kong)

^b School of Materials Science and Engineering   (China)

Author keywords

Air handling process; Fault detection and diagnosis; Multivariate decoupling; Principle component analysis; Sensor fault

Indexed keywords

AIR CONDITIONING; ELECTRIC FAULT CURRENTS; FREQUENCY DIVISION MULTIPLEXING; NEURAL NETWORKS; ORTHOGONAL FREQUENCY DIVISION MULTIPLEXING; PRINCIPAL COMPONENT ANALYSIS; SENSORS; VECTORS;

AIR-CONDITIONING SYSTEMS; COMPONENT FAULTS; DECOUPLING METHODS; DETECTING SENSORS; EXPERT KNOWLEDGES; FAULT DETECTION AND DIAGNOSES; FAULT DETECTION AND DIAGNOSIS; FAULT DIAGNOSIS; FAULT ISOLATIONS; FAULT PATTERNS; FAULT SYMPTOMS; HANDLING PROCESSES; MULTIPLE VARIABLES; MULTIVARIATE DECOUPLING; MULTIVARIATE PROCESSES; OF SENSORS; PCA METHODS; PCA MODELS; PRINCIPAL COMPONENTS; PRINCIPLE COMPONENT ANALYSIS; RESIDUAL VECTORS; SENSOR FAULT; SENSOR FAULTS; TEST RESULTS; VARIABLE AIR VOLUME AIR CONDITIONING SYSTEMS;

FAULT DETECTION;

EID: 56049083538     PISSN: 13594311     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.applthermaleng.2008.03.046     Document Type: Article

References (29)

1. Edward J.J. A User's Guide to Principal Components (1991), John Wiley
2. Jolliffe I.T. Principal Component Analysis (1986), Springer-Verlag, New York
3. Dunia R., Qin S.J., Edgar T.E., and McAvoy T.J. Identification of faulty sensors using principal component analysis. AIChE Journal 104 1 (1996) 2797-2812
4. Dunia R., and Qin S.J. Joint diagnosis of process and sensor faults using principal component analysis. Control Engineering Practice 6 (1998) 457-469
5. Russell E.L., Chiang L.H., and Richard D.B. Fault detection in industrial processes using canonical variant analysis and dynamic principal component analysis. Chemometrics and Intelligent Laboratory Systems 21 1 (2000) 81-93
6. Cho J.H., et al. Fault identification for process monitoring using kernel principal component analysis. Chemical Engineering Science 60 1 (2005) 279-288
7. Li W.H., Yue H.H., Valle-Cervantes S., and Qin S.J. Recursive PCA for adaptive process monitoring. Journal of Process Control 10 (2000) 471-486
8. Tsung F. Statistical monitoring and diagnosis of automatic controlled processes using dynamic PCA. International Journal of Production Research 38 3 (2000) 625-637
9. Lennox J., and Rosen C. Adaptive multiscale principal components analysis for online monitoring of wastewater treatment. Water Science and Technology 45 4-5 (2002) 227-235
10. Kaistha N., and Upadhyaya B.R. Incipient fault detection and isolation of field devices in nuclear power systems using principal component analysis. Nuclear Technology 136 2 (2001) 221-230
11. Wang S.W., and Xiao F. Detection and diagnosis of AHU sensor faults using principal component analysis method. Energy and Buildings 36 (2004) 147-160
12. Xiao F., Wang S.W., and Zhang J.P. A diagnostic tool for online sensor health monitoring in air-conditioning systems. Automation in Construction 15 (2006) 489-503
13. Wang S.W., and Chen Y.M. Sensor validation and reconstruction for building central chilling systems based on principal component analysis. Energy Conservation and Management 45 (2004) 673-695
14. Gertler J., Li W.H., Huang Y.B., and McAvoy T. Isolation enhanced principal component analysis. AIChE Journal 45 2 (1999) 323-334
15. Vedam H., and Venkatasubramanian V. PCA-SDG based process monitoring and fault diagnosis. Control Engineering Practice 7 (1999) 903-917
16. Yoon S., and MacGregor J.F. Fault diagnosis with multivariate statistical models part I: using steady state fault signatures. Journal of Process Control 11 (2001) 387-400
17. B.M. Wise, N.L. Ricker, Recent advances in multivariate statistical process control: improving robustness and sensitivity, in: K. Najim, J.P. Parbary (Eds.), IFAC International Symposium, ADCHEM'91, Toulouse, France, 1991, pp. 125-130.
18. Jin X.Q., and Du Z.M. Fault tolerant control of outdoor air and AHU supply air temperature in VAV air conditioning systems using PCA method. Applied Thermal Engineering 26 (2006) 1226-1237
19. Du Z.M., Jin X.Q., and Wu L.Z. Fault detection and diagnosis based on improved PCA with JAA method in VAV systems. Building and Environment 42 (2007) 3221-3232
20. Wang S.W. Dynamic simulation of building VAV air-conditioning system and evaluation of EMCS Online Control Strategies. Building and Environment 34 (1999) 681-705
21. Wang S.W., and Xu X.H. A robust control strategy for combining DCV control with economizer control. Energy Conversion and Management 43 (2002) 2569-2588
22. Avery G. The instability of VAV systems. Heating/Piping/Air Conditioning 64 (1992) 47-50
23. Wang J., and Wang Y. Performance improvement of VAV air conditioning system through feed forward compensation decoupling and genetic algorithm. Applied Thermal Engineering 28 5-6 (2008) 566-574
24. R.G. Carlos, V.R. Miguel, Decoupled control of temperature and relative humidity using a variable-air-volume HVAC system and non-interacting control, in: IEEE Conference on Control Applications-Proceedings, 2001, pp. 1147-1151.
25. Vedam H., and Venkatasubramanian V. Signed diagraph based multiple fault diagnosis. Computer and Chemical Engineering 21 S1 (1997) 655-660
26. Luo F.L., and Wen C.Y. Multiple-page mapping artificial neural network algorithm used for constant tension control. Expert Systems with Applications 13 4 (1997) 307-315
27. Oliva A.P., and Leite W.C. Eigenstructure versus optimal control for decoupling. Control Engineering Practice 10 10 (2002) 1059-1079
28. H.L. Shu, X.C. Guo, Decoupling control of multivariable time-varying systems based on PID neural network, in: Proceedings of 5th Asian Control Conference, vol. 1. 2004, pp. 682-685.
29. Chatelin F. Eigenvalues of Matrices (1993), Wiley, New York