Data Reconciliation and Gross Error Detection. An by Dr. Shankar Narasimhan Ph.D. (Ch.E.)

By Dr. Shankar Narasimhan Ph.D. (Ch.E.)

  • This is a wonderful e-book at the topic - the authors have coated all of the bases. if you'd like a booklet on facts reconciliation and gross errors detection, this can be as entire and thorough a e-book as i will be able to think. - Les A. Kane, Editor, complex procedure regulate and knowledge platforms


, Pages xiii-xiv

, Pages xv-xvii
1 - the significance of information Reconciliation and Gross blunders Detection

, Pages 1-31
2 - size blunders and blunder relief Techniques

, Pages 32-58
3 - Linear Steady-State information Reconciliation

, Pages 59-84
4 - Steady-State facts Reconciliation for Bilinear Systems

, Pages 85-118
5 - Nonlinear Steady-State facts Reconciliation

, Pages 119-141
6 - info Reconciliation in Dynamic Systems

, Pages 142-173
7 - creation to Gross blunders Detection

, Pages 174-225
8 - a number of Gross errors identity suggestions for Steady-State Processes

, Pages 226-280
9 - Gross errors Detection in Linear Dynamic Systems

, Pages 281-299
10 - layout of Sensor Networks

, Pages 300-326
11 - business purposes of knowledge Reconciliation and Gross blunders Detection Technologies

, Pages 327-372
Appendix A - easy strategies in Linear Algebra

, Pages 373-377
Appendix B - Graph conception Fundamentals

, Pages 378-383
Appendix C - basics of likelihood and Statistics

, Pages 384-393

, Pages 394-402
Author Index

, Pages 403-405
The Authors

, Page 406

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Extra info for Data Reconciliation and Gross Error Detection. An Intelligent Use of Process Data

Sample text

It is easier to implement, and, with proper tuning, enables a faster state-change detection than the CUSUM tests. For this reason, the Shewhart test is often used in association with CUSUM tests in statistical process control [ 17]. Five major signal types can be determined with their algorithm: steady-state, step change, ramp, impulse (spike), and undetermined transient state. The filter is forced to follow more closely the process dynamics in the case of a true process change and to do more filtering in the case of the random noise (steady state) or spikes.

Peck. Introduction to Linear Regression Analysis. New York: John Wiley & Sons, 1982. 17. Lucas, J. M. "Combined ShewhartmCUSUM Quality Control Schemes," Journal of Quality Technology 14 (no. 2, 1982): 51-59. 18. Rinehart, R. R. " Process Control and Quality (Amsterdam: Elsevier) (no. 2, 1992): 169-176. 19. Stanley, G. M. , Oct. 1, 1992. 3 Linear Steady-State Data Reconciliation Linear data reconciliation for steady-state systems has already been introduced in Chapter 1. The examples analyzed in Chapter 1 are instances of a linear data reconciliation problem.

L. " Chem. Eng. Progr. Symp. Ser. No. 55 61 (1965): 8-13. 23. Reilly, P. , and R. E. Carpani. "Application of Statistical Theory to Adjustment of Material Balances," presented at the 13th Can. Chem. Eng. , Montreal, Quebec, 1963. 24. Almasy, G. A, and T. Sztano. " Prob. Control Inform. Theory 4 (1975): 57-69. 25. H. Mah. " AIChE Journal 33 (1987): 1514-1521. 26. Tamhane. A. , C. H. Mah. "A Bayesian Approach to Gross Error Detection in Chemical Process Data. " Chemometrics and Intel. Lab. Sys. 4 (1988): 33.

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