Fault Detection and Diagnosis via Improved Statistical Process Control: Chemical Process Application

Harun, Noorlisa, Ibrahim, Kamarul Asri

ISBN 10: 3659177652 / ISBN 13: 9783659177651

Published by LAP LAMBERT Academic Publishing, 2012

About this Item

LIKE NEW. SHIPS FROM MULTIPLE LOCATIONS.

Seller Inventory # ERICA79636591776526

Bibliographic Details

• Title: Fault Detection and Diagnosis via Improved Statistical Process Control: Chemical Process Application
• Publisher: LAP LAMBERT Academic Publishing
• Publication Date: 2012
• Language: English
• ISBN 10: 3659177652
• ISBN 13: 9783659177651
• Binding: Paperback
• Condition: Like New
• Book Type: book

About this title

Synopsis

Multivariate Statistical Process Control (MSPC) technique has been widely used for fault detection and diagnosis (FDD). Currently, contribution plots are used as basic tools for fault diagnosis in MSPC approaches. This plot does not exactly diagnose the fault, it just provides greater insight into possible causes and thereby narrow down the search. Hence, the cause of the faults cannot be found in a straightforward manner. Therefore, this study is conducted to introduce a new approach for detecting and diagnosing fault via correlation technique. Multivariate analysis technique i.e Principal Component Analysis, PCA and Partial Correlation Analysis, PCorrA are utilized to determine the correlation coefficient between quality variables and process variables. A precut multicomponent distillation column that has been installed with controllers is used as the study unit operation. Improved SPC method is implemented to detect and diagnose various kinds of faults, which occur in the process. Individual charting technique such as Shewhart, Exponential Weight Moving Average (EWMA) and Moving Average and Moving Range (MAMR) charts are used to facilitate the FDD.

About the Author

Noorlisa Harun received her B.Eng. (Chem.) and M.Sc. (Chem.) from Universiti Teknologi Malaysia (UTM), and PhD in chemical engineering from University of Waterloo, Canada.  Currently, Noorlisa is a senior lecturer at Universiti Malaysia Pahang and a member of Process Systems Engineering Group (PSEG).