The impact of compression on data-driven process analyses

Journal of Process Control

Volumn 14, Issue 4, 2004, Pages 389-398

  Thornhill, Nina F ^a   Shoukat Choudhury, M A A ^b   Shah, Sirish L ^b  

^a UNIVERSITY COLLEGE LONDON   (United Kingdom)

^b UNIVERSITY OF ALBERTA   (Canada)

Author keywords

Chemical process; Controller performance monitoring; Data compression; Data reconciliation; Fault detection; Non linearity; Process monitoring; Process trend; Spectrum; Statistical process control

Indexed keywords

ALGORITHMS; CONTROL EQUIPMENT; DATABASE SYSTEMS; ERROR ANALYSIS; FAULT TOLERANT COMPUTER SYSTEMS; FEATURE EXTRACTION; MATHEMATICAL TRANSFORMATIONS; NONLINEAR CONTROL SYSTEMS; STATISTICAL PROCESS CONTROL;

ARITHMETICAL PRECISION; DATA-DRIVEN ANALYSES; FAULT DETECTION;

DATA COMPRESSION;

EID: 0346119933     PISSN: 09591524     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jprocont.2003.06.003     Document Type: Article

References (31)

1. J.P. Kennedy, Data treatment and applications - future of the desktop, in: Proceedings of FOCAPO, CACHE, 1993.
2. AspenTech, Analysis of data storage technologies for the management of real-time process manufacturing data, Retrieved July 19th 2003, from http://www.advanced-energy.com/Upload/symphony_wp_infoplus.pdf, 2001.
3. OSI Software Inc., PI data storage component overview, Retrieved July 19th 2003, from http://www.osisoft.com/ 270.htm, 2002.
4. Watson A.B. Digital Images and Human Vision. 1993;MIT Press.
5. Crowe J.A., Gibson N.M., Woolfson M.S., Somekh M.G. Wavelet transform as a potential tool for ECG analysis and compression. Journal of Biomedical Engineering. 14:1992;268-272.
6. Philips W. ECG data compression with time warped polynomials. IEE Transaction on Biomedical Engineering. 40:1993;1095-1101.
7. Karczewicz M., Gabbouj M. ECG data compression by spline approximation. Signal Processing. 59:1997;43-59.
8. Mah R.S.H., Tamhane A.C., Tung S.H., Patel A.N. Process trending with piecewise linear smoothing. Computers and Chemical Engineering. 19:1995;129-137.
9. Watson M.J., Liakopoulos A., Brzakovic D., Georgakis C. A practical assessment of process data compression techniques. Industrial and Engineering Chemistry Research. 37:1998;267-274.
10. Hale J.C., Sellars H.L. Historic data recording for process computers. Chemical Engineering Progress. 77(November):1981;38-43.
11. E.H. Bristol, Swinging door trending: adaptive trend recording, in: ISA National Conference Proceedings, 1990, pp. 749-753.
12. Donoho D.L., Vetterli M., DeVore R.A., Daubechies I. Data compression and harmonic analysis. IEEE Transactions on Information Theory. 44:1998;2435-2476.
13. Nesic Z., Davies M., Dumont G. Paper machine data analysis and compression using wavelets. Tappi Journal. 80:1997;191-203.
14. V.K. Raghavan, J.R. Whiteley, Wavelet representation of sensor patterns for monitoring and control, in: AIChE Annual Meeting, St Louis, Paper 150b6, 1993.
15. Bakshi B., Stephanopoulos G. Compression of chemical process data through functional approximation and feature extraction. AIChE Journal. 42:1996;477-492.
16. Misra M., Kumar S., Qin S.J., Seeman D. Error based criterion for on-line wavelet data compression. Journal of Process Control. 11:2001;717-731.
17. Misra M., Kumar S., Qin S.J., Seeman D. On-line data compression and error analysis using wavelet technology. AIChE Journal. 46:2000;119-132.
18. Vedam H., Venkatasubramanian V., Bhalodia B. A B-spline based method for data compression, process monitoring and diagnosis. Computers and Chemical Engineering. 22:1998;S827-S830.
19. Welch P.D. The use of fast Fourier transforms for the estimation of power spectra. IEEE Transactions on Audio and Electroacoustics AU. 15:1967;70-73.
20. Martin G.D., Turpin L.E., Cline R.P. Estimating control function benefits. Hydrocarbon Processing. 70(June):1991;68-73.
21. Shunta J.P. Achieving World Class Manufacturing Through Process Control. 1995;Prentice-Hall, NJ.
22. M.A.A.S. Choudhury, S.L. Shah, N.F. Thornhill, Diagnosis of poor control loop performance using higher order statistics, Automatica, in press.
23. H.E. Emara-Shabaik, J. Bomberger, D.E. Seborg, Cumulant/bispectrum model structure identification applied to a pH neutralization process, in: IEE Proceedings, UKACC International Conference on Control'96, UK, 1996, pp. 1046-1051.
24. Barnard J.P., Aldrich C., Gerber M. Identification of dynamic process systems with surrogate data methods. AIChE Journal. 47:2001;2064-2075.
25. M.A.A.S. Choudhury, S.L. Shah, N.F. Thornhill, Detection and diagnosis of system nonlinearities using higher order statistics, in: IFAC World Congress, 2002.
26. Desborough L., Harris T. Performance assessment measures for univariate feedback control. Canadian Journal of Chemical Engineering. 70:1992;1186-1197.
27. Paulonis M.A., Cox J.W. A practical approach for large-scale controller performance assessment, diagnosis, and improvement. Journal of Process Control. 13:2003;155-168.
28. L. Desborough, R. Miller, Increasing customer value of industrial control performance monitoring - Honeywell's experience, in: AIChE Symposium Series No. 326, vol. 98, 2002, pp. 153-186.
29. P. Fedenczuk, P. Fountain, R. Miller, Loop Scout, RPID and Profit Controller team up to produce significant benefits for BP, Retrieved July 19th 2003, from http://loopscout.com/loopscout/info/bpamoco.pdf, 1999.
30. Thornhill N.F., Oettinger M., Fedenczuk P. Refinery-wide control loop performance assessment. Journal of Process Control. 9:1999;109-124.
31. Wetherill G.B., Brown D.W. Statistical Process Control. 1991;Chapman and Hall, London.