Get Multivariate Statistical Process Control: Process Monitoring PDF

By Zhiqiang Ge

ISBN-10: 1447145127

ISBN-13: 9781447145127

ISBN-10: 1447145135

ISBN-13: 9781447145134

Given their key place within the strategy regulate undefined, method tracking options were widely investigated by way of commercial practitioners and educational keep watch over researchers. Multivariate statistical method keep watch over (MSPC) is among the most well-liked data-based equipment for method tracking and is normal in a number of commercial parts. potent workouts for strategy tracking can assist operators run business methods successfully even as holding excessive product caliber.

Multivariate Statistical procedure Controlreviews the advancements and enhancements which were made to MSPC during the last decade, and is going directly to suggest a sequence of recent MSPC-based methods for complicated method tracking. those new tools are tested in numerous case stories from the chemical, organic, and semiconductor commercial components.

Control and approach engineers, and educational researchers within the procedure tracking, technique keep an eye on and fault detection and isolation (FDI) disciplines can be attracted to this publication. it could possibly even be used to supply supplementary fabric and commercial perception for graduate and complicated undergraduate scholars, and graduate engineers.

Advances in commercial Control goals to file and inspire the move of expertise up to speed engineering. The fast improvement of regulate know-how has an effect on all parts of the keep watch over self-discipline. The sequence bargains a chance for researchers to offer a longer exposition of latest paintings in all elements of business control.

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Extra info for Multivariate Statistical Process Control: Process Monitoring Methods and Applications

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For comparison, the ICA, PCA and ICA-PCA algorithms are also carried out. The number of ICs for ICA is selected as 3, whereas the PCs number for PCA is chosen as 6. All confidence limits are set as 99 %. The scatter plot of the first independent component versus the second independent component and two confidence limits of NGS and I 2 is given in Fig. 5. Under normal operation, both of the confidence limits indicate that 99 % samples are inside their corresponding regions. However, as discussed previously, the confidence limit of NGS is tighter than that of I 2 , which is shown correctly in the figure.

Change of any type or types of information will be reflected in related monitoring charts. 3 Case Study In this section, the performance of the method is tested through the TE process. The variables selected for monitoring are the same as those used for control structure 26 3 Non-Gaussian Process Monitoring Fig. 5 Scatter plot of TE normal operation condition 3 2 s2 1 0 -1 -2 -3 -2 0 s1 2 4 development in Lyman and Georgakist (1995). Simulation data which are acquired in the normal process consist of 960 samples, and their sampling interval was 3 min.

In contrast, if an incorrect direction is applied {Fi , i = 1, 2, . . , J , i = j }, the univariate NGS i statistic still violates its control limit. Calculating the non-Gaussian statistic value NGS j by using Eqs. 2 Fault Reconstruction 33 which is referred to here as the fault diagnosis index and NGS lim is the control limit of the NGS statistic. If the reconstruction procedure has been applied using the correct fault direction, Eq. 14) highlights that a fault diagnosis index produces a value that is smaller than or equal to 1.

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Multivariate Statistical Process Control: Process Monitoring Methods and Applications by Zhiqiang Ge

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