Faulty sensor detection and reconstruction for a PVC making process

Chinese Journal of Chemical Engineering

Volumn 12, Issue 2, 2004, Pages 227-233

  Li, Yuan ^a,b,c   Zhou, Donghua ^c   Xie, Zhi ^b   Qin, S Joe ^d  

^a NORTHEASTERN UNIVERSITY   (China)

^b SHENYANG UNIVERSITY OF CHEMICAL TECHNOLOGY   (China)

^c TSINGHUA UNIVERSITY   (China)

^d The University of Texas at Austin   (United States)

Author keywords

Dynamic time warping; Faulty sensor detection; Faulty sensor reconstruction; Multi way principal component analysis

Indexed keywords

CHEMICAL SENSORS; PRINCIPAL COMPONENT ANALYSIS; PROCESS CONTROL;

DYNAMIC TIME WARPING; FAULTY SENSOR DETECTION; FAULTY SENSOR RECONSTRUCTION; MULTIWAY PRINCIPAL COMPONENT ANALYSIS;

POLYVINYL CHLORIDES;

SENSOR;

EID: 3142577148     PISSN: 10049541     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (20)

1. Nomikos, P., MacGregor, J.F., Multi-way partial least squares in monitoring batch processes, Chemom. Intell. Lab. Syst., 30, 97-108 (1995).
2. Nomikos, P., MacGregor, J.F., Multivariable SPC charts for monitoring batch process, Technometrics, 37 (1), 41-59 (1995).
3. Miller, P., Swanson, R.E., Heckler, C.F., Contribution plots: the missing link in multivariate quality control, In: Fall Conf. Of the ASQC and ASA. Milwaukee, WI (1993).
4. Kosanovich, K.A., Dahl, K.S., Piovoso, M.J., Improved process understanding using multi-way principal component analysis, Ind. Eng. Chem. Res., 35, 138-146 (1996).
5. Dunia, R., Qin, S.J., Edgar, T.F., MacAvoy, T.J., Identification of faulty sensors using principal component analysis, AIChE J., 42 (10), 2797-2812 (1996).
6. Duina, R., Qin, S.J., Joint diagnosis of process and sensor faults using principal component analysis, Control Eng. Practice, 6 (4), 457-469 (1998).
7. Qin, S.J, Duina, R., Determining the number of principal components for best reconstruction, J. Process Control, 10 (2-3), 245-250 (2000).
8. Duina, R., Qin, S.J., A unified geometric approach to process and sensor fault identification: the unidimensional fault case, Comput. Chem. Eng., 22 (7-8), 927-943 (1998).
9. Lewin, D.R., Bogle, D., Controllability analysis of an industrial polymerization reactor, Comput. Chem. Eng., 20 (Suppl), S871-S876 (1996).
10. Lewin, D.R., Modeling and control of an industrial PVC suspension polymerization reactor, Comput. Chem. Eng., 20 (Suppl.), S865-S870 (1996).
11. Nomikos, P., MacGregor, J.F., Monitoring batch process using multi-way principle component analysis, AIChE J., 40 (8), 1361-1375 (1994).
12. Dahl, K.S., Piovoso, M.J., Kosanovich, K.A., Translating third-order data analysis methods to chemical batch processes, Chemom. Intell. Lab. Syst., 46, 161-180 (1999).
13. Myers, C., Rabiner, L.R., Rosenberg, A.E., Performance tradeoffs in dynamic time warping algorithms for isolated word recognition, IEEE Trans. Acoustics, Speech and Signal Processing, 28 (6), 623-635 (1980).
14. Rabiner, L.R., Rosenberg, A.E., Levinson, S.E., Considerations in dynamic time warping algorithms for discrete word recognition, IEEE Trans. Acoustics. Speech and Signal Processing, 26 (6), 575-582 (1978).
15. Itkura, F., Minimum prediction residual principle applied to speech recognition, IEEE Trans. Acoustics, Speech and Signal Processing, 23 (1), 67-72 (1975).
16. Sakoe, H., Chiba, S., Dynamic programming algorithm optimization for spoken word recognition, IEEE Trans. Acoustics, Speech and Signal Processing, 26 (1), 43-49 (1978).
17. Kassidas, A., MacGregor, J.F., Taylor, P.A., Synchronization of batch trajectories using dynamic time warping, AIChE J., 44 (4), 864-875 (1998).
18. Zhou, D.H., Ye, Y.Z., Modern Fault Diagnosis and Tolerance Control, Tsinghua University Press, Beijing (2000). (in Chinese)
19. Kassidas, A., Taylor, P.A., MacGregor, J.F., Off-line diagnosis of deterministic faults in continuous dynamic multivariable processes using speech recognition methods, J. Process Control, 8 (5-6), 381-393 (1998).
20. Johnson, R.A., Wichern, D.W., Applied Multivariate Statistical Analysis, Prentice-Hall (1998).