By Evan L. Russell PhD, Leo H. Chiang MS, Richard D. Braatz PhD (auth.)
Early and exact fault detection and analysis for contemporary chemical crops can minimise downtime, bring up the protection of plant operations, and decrease production expenditures. The process-monitoring concepts which have been premiere in perform are in keeping with versions built virtually fullyyt from technique info. The target of the ebook is to offer the theoretical historical past and useful suggestions for data-driven technique tracking. Process-monitoring suggestions provided contain: relevant part research; Fisher discriminant research; Partial least squares; Canonical variate analysis.
The textual content demonstrates the appliance of the entire data-driven technique tracking options to the Tennessee Eastman plant simulator - demonstrating the strengths and weaknesses of every procedure intimately. This aids the reader in selecting the best strategy for his method software. Plant simulator and homework difficulties during which scholars observe the process-monitoring ideas to a nontrivial simulated approach, and will examine their functionality with that acquired within the case reviews within the textual content are integrated. a few extra homework difficulties motivate the reader to enforce and procure a deeper realizing of the techniques.
The reader will receive a historical past in data-driven thoughts for fault detection and analysis, together with the facility to enforce the thoughts and to grasp how one can choose the appropriate process for a selected application.
Read or Download Data-driven Methods for Fault Detection and Diagnosis in Chemical Processes PDF
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Extra resources for Data-driven Methods for Fault Detection and Diagnosis in Chemical Processes
The scree test, 3. parallel analysis, and 4. the PRESS statistic. The percent variance method determines a by calculating the smallest number of loading vectors needed to explain a specific minimum percentage 38 4. ,---~~~ 12 : ........................ ·· .... ·..... : .... ·· .. ·.......... :.. ·.. : .. a 0 0 ·~ · Q; .. i 0 . ~ ~;;o'tJc9 0 ~ 0 10 ...... ....... .. .... ~ . .... OO( .. -¥,BOO .... O..... : ..... . O : ~ 0 ~ : : .... . :~~t::'(~r •••••••••••••••• r• ••••••••••••••• .. • K · .
________ ________- L , , ________ ~ 0 ~ 5 t, 10 ~ 15 ~ 20 Fig. 2. The projections of experimental data [50, 25J for three classes onto the first two PCA loading vectors of the total variance. ) Because this minimum percentage is chosen arbitrarily, it may be too low or too high for a particular application. The scree test assumes that the variance, ul, corresponding to the random noise forms a linear profile. The dimension of the score space a is determined by locating the value of ul where the profile is no longer linear.
44) becomes: X(h) = Yt Ut Yt-l Ut-l Yt-l Ut-l Yt-2 Ut-2 Yt-h Ut-h ... 46) YHh-n uHh-n YHh-n-l uHh-n-l . . Yt-n Ut-n The ARX(h) model indicates that the first column of X(h) is linearly related to the remaining columns. , not full rank). 3), the eigenvector corresponding to the zero eigenvalue would reveal the ARX(h) correlation structure . In the case where noise is present, the matrix will be nearly rank deficient. The eigenvector corresponding to a nearly zero eigenvalue will be an approximation of the ARX(h) correlation structure [125, 165].
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