By Dr. Shankar Narasimhan Ph.D. (Ch.E.)
This is a superb ebook at the topic - the authors have lined the entire bases. if you would like a e-book on info reconciliation and gross blunders detection, this is often as entire and thorough a ebook as i will think. - Les A. Kane, Editor, complicated procedure keep an eye on and data platforms
, Pages xiii-xiv
, Pages xv-xvii
1 - the significance of information Reconciliation and Gross blunders Detection
, Pages 1-31
2 - size mistakes and blunder aid Techniques
, Pages 32-58
3 - Linear Steady-State info Reconciliation
, Pages 59-84
4 - Steady-State info Reconciliation for Bilinear Systems
, Pages 85-118
5 - Nonlinear Steady-State information Reconciliation
, Pages 119-141
6 - facts Reconciliation in Dynamic Systems
, Pages 142-173
7 - creation to Gross mistakes Detection
, Pages 174-225
8 - a number of Gross errors identity suggestions for Steady-State Processes
, Pages 226-280
9 - Gross blunders Detection in Linear Dynamic Systems
, Pages 281-299
10 - layout of Sensor Networks
, Pages 300-326
11 - commercial functions of knowledge Reconciliation and Gross errors Detection Technologies
, Pages 327-372
Appendix A - easy thoughts in Linear Algebra
, Pages 373-377
Appendix B - Graph thought Fundamentals
, Pages 378-383
Appendix C - basics of chance and Statistics
, Pages 384-393
, Pages 394-402
, Pages 403-405
, Page 406
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Extra resources for Data Reconciliation and Gross Error Detection. An Intelligent Use of Process Data
The former estimates the current value based on the current and past measurements and it is of primary concern in process control. The latter estimates the value of the central point from past and recent measurements (values from both sides of the central point) and it is mainly used for fault diagnosis and steady-state process optimization. Many authors, however, do not distinguish between the two terms and use the data "smoothing" term for data filtering as well. An integral of absolute errors (IAE) similar to that of Kim and Lee  will be used to compare various filtering techniques in this text.
The E W M A filter is analytically described as : (2-24) Yk - ~Yk + (1 -- ~) Yk-1 where Yk = sample mean (moving average with equal weights) at time tk Yk = filtered value at time tk ~, = filter parameter ; 0 < ~, < 1 Initially, Y0 is taken as the control target kt0 ( Y0 = kt0). to k - 1 , 2 , 3 ..... (2-25) j=O Equation 2-25 indicates that the weights assigned to the sample means decrease geometrically with age. For that reason, this filter is sometimes referred to as the geometric moving average filter .
Is there any incentive to use the more complex filter weights given by Equation 2-23 rather than a simple moving average filter with equal weights ? Note that the filter with unequal exponential weights described above by Equation 2-22 and 2-23 is to be distinguished from the exponentially weighted moving average (EWMA)filter which is often used in statistical process control area. The E W M A filter is analytically described as : (2-24) Yk - ~Yk + (1 -- ~) Yk-1 where Yk = sample mean (moving average with equal weights) at time tk Yk = filtered value at time tk ~, = filter parameter ; 0 < ~, < 1 Initially, Y0 is taken as the control target kt0 ( Y0 = kt0).
Data Reconciliation and Gross Error Detection. An Intelligent Use of Process Data by Dr. Shankar Narasimhan Ph.D. (Ch.E.)