Fault detection and diagnosis using principal component analysis. Application to low pressure lost foam casting process

International Journal of Modelling, Identification and Control

Volumn 14, Issue 1-2, 2011, Pages 102-111

  Bendjama, Hocine ^a,b   Boucherit, Mohamed Seghir ^b   Bouhouche, Salah ^a   Bast, Jürgen ^c  

^a Centre Recherche Scientifique et Technique en Soudage et Controle   (Algeria)

^b ECOLE NATIONALE POLYTECHNIQUE   (Algeria)

^c TU BERGAKADEMIE FREIBERG   (Germany)

Author keywords

Fault detection and diagnosis; PCA; Principal component analysis; Q residual contribution; Q statistic; SPE; Squared prediction error

Indexed keywords

MULTIVARIANT ANALYSIS; PRINCIPAL COMPONENT ANALYSIS; STATISTICAL PROCESS CONTROL; STATISTICS;

FAULT DETECTION AND DIAGNOSIS; LOST FOAM CASTING PROCESS; MULTIVARIATE STATISTICAL PROCESS CONTROL; PROCESS FAULT DETECTION; Q STATISTICS; Q-RESIDUAL CONTRIBUTION; SQUARED PREDICTION ERRORS; STATISTICAL CORRELATION;

FAULT DETECTION;

EID: 80052482285     PISSN: 17466172     EISSN: 17466180     Source Type: Journal    
DOI: 10.1504/IJMIC.2011.042344     Document Type: Article

References (23)

1. Bast, J., Aitsuradse, M. and Hahn, T. (2004) 'Advantages of the Low Pressure Lost Foam Casting Process', AFS Transactions, Vol. 112, pp.210-270.
2. Chadli, M., Akhenak, A., Maquin, D. and Ragot, J. (2008) 'Fuzzy observer for fault detection and reconstruction of unknown input fuzzy models', International Journal of Modelling, Identification and Control, Vol. 3, No. 2, pp.193-200.
3. Chiang, L.H., Russell, E.L. and Braatz, R.D. (2001) Fault Detection and Diagnosis in Industrial Systems, Springer Verlag, London.
4. Dexter, A.L. and Ngo, D. (2001) 'Fault diagnosis in air-conditioning system: a multi-step fuzzy model-based approach', HVAC & R Res, Vol. 7, No. 1, pp.83-102. (Pubitemid 32254228)
5. Jackson, J.E. and Mudholkar, G.S. (1979) 'Control procedures for residuals associated with principal components analysis', Technometrics, Vol. 21, pp.341-349. (Pubitemid 9472204)
6. Jolliffe, I.T. (2002) Principal Component Analysis, Springer Verlag, New York.
7. Kano, M. and Hasebe, S. (2001) 'A new multivariate statistical process monitoring method using principal component analysis', Computers & Chemical Engineering, Vol. 25, pp.1103-1113. (Pubitemid 32772290)
8. Kano, M., Nagao, K., Hasebe, S., Hashimoto, I., Ohno, H. and Strauss, R. (2001) 'Comparison of multivariate statistical process monitoring methods with applications to the Eastman challenge problem', Computers and Chemical Engineering, Vol. 26, pp.161-174. (Pubitemid 34211482)
9. Kaszkurewicz, E., Bhaya, A. and Ebecken, N.F.F. (1997) 'A fault detection and diagnosis module for oil production plants in offshore platforms', Expert Syst Appl, Vol. 12, No. 2, pp. 189-194. (Pubitemid 127379358)
10. Lang, L. (1999) 'Development and testing of a low pressure lost foam casting machine and an examination of the process', PhD thesis, Freiberg University, Germany.
11. McGhee, J., Henderson, I.A. and Baird, A. (1997) 'Neural networks applied for the identification and fault diagnosis for the process valves and actuators', Measurement, Vol. 20, No. 4, pp.267-275. (Pubitemid 127397970)
12. Nomikos, P. and MacGregor, J.F. (1995) 'Multivariate SPC charts for monitoring batch processes', Technometrics, Vol. 37, No. 1, pp.41-59.
13. Rajakarunakaran, S., Devaraj, D. and Suryaprakasa Rao, K. (2008) 'Fault detection in centrifugal pumping systems using neural networks', International Journal of Modelling, Identification and Control, Vol. 3, No. 2, pp.131-139.
14. Sangha, M.S., Barry Gomm, J. and Yu, D. (2008) 'Neural network fault classification of transient data in an automotive engine air path', International Journal of Modelling, Identification and Control, Vol. 3, No. 2, pp.148-155.
15. Staroswiecki, M. and Comete-Varga, G. (2001) 'Analytical redundancy relations for fault detection and isolation in algebraic dynamic systems', Automatica, Vol. 37, No. 5, pp.687-699. (Pubitemid 32264797)
16. Sun, X., Marquez, H.J. and Chen, T. (2005) 'An improved PCA method with application to boiler leak detection', ISA Transactions, Vol. 44, pp.379-397. (Pubitemid 41422405)
17. Tamura, M. and Tsujita, S. (2007) 'A study on the number of principal components and sensitivity of fault detection using PCA', Computers and Chemical Engineering, Vol. 31, pp.1035-1046. (Pubitemid 46779125)
18. Tsai, C.Y. and Chiu, C. (2008) 'An efficient conserved region detection method for multiple protein sequences using principal component analysis and wavelet transform', Pattern Recognition Letters, Vol. 29, pp.616-628.
19. Wang, S. and Xiao, F. (2004) 'Detection and diagnosis of AHU sensor faults using principal component analysis method', Energy Conversion and Management, Vol. 45, pp.2667-2686.
20. Xu, X., Xiao, F. and Wang, S. (2008) 'Enhanced chiller sensor fault detection, diagnosis and estimation using wavelet analysis and principal component analysis methods', Applied Thermal Engineering, Vol. 28, pp.226-237. (Pubitemid 47576718)
21. Ying, C.M. and Joseph, B. (2000) 'Sensor fault detection using noise analysis', Industrial and Engineering Chemistry Research, Vol. 39, No. 2, pp.396-407. (Pubitemid 30114583)
22. Yoshida, H. and Kumar, S. (1999) 'ARX and AFMM model based online real time database diagnosis of sudden fault in AHU of VAV system', Energy Convers Manage, Vol. 40, No. 11, pp.1191-1206.
23. Zuo, M.J., Lin, J. and Fan, X. (2005) 'Feature separation using ICA for a one-dimensional times series and its application in fault detection', Journal of sound and vibration, Vol. 287, pp.614-624. (Pubitemid 41231675)