Fault detection and diagnosis for gas density monitoring using multivariate statistical process control

Malfunction of plant equipment, instrumentation and degradation in process operation increase the operating cost of any chemical process industries. Thus, modern chemical industries need to operate as fault free as possible because faults that present in a process increase the operating cost due to...

Full description

Bibliographic Details
Main Authors:	N. S., Che Din, Noor Asma Fazli, Abdul Samad, Chin, S. Y.
Format:	Article
Language:	English
Published:	Asian Network for Scientific Information 2011
Subjects:	TP Chemical technology
Online Access:	http://umpir.ump.edu.my/id/eprint/24964/ http://umpir.ump.edu.my/id/eprint/24964/ http://umpir.ump.edu.my/id/eprint/24964/ http://umpir.ump.edu.my/id/eprint/24964/1/Fault%20detection%20and%20diagnosis%20for%20gas%20density%20monitoring%20using%20multivariate%20statistical%20process%20control.pdf

id	ump-24964
recordtype	eprints
spelling	ump-249642019-11-12T04:23:23Z http://umpir.ump.edu.my/id/eprint/24964/ Fault detection and diagnosis for gas density monitoring using multivariate statistical process control N. S., Che Din Noor Asma Fazli, Abdul Samad Chin, S. Y. TP Chemical technology Malfunction of plant equipment, instrumentation and degradation in process operation increase the operating cost of any chemical process industries. Thus, modern chemical industries need to operate as fault free as possible because faults that present in a process increase the operating cost due to the increase in waste generation and products with undesired specifications. Effective monitoring strategy for early fault detection and diagnosis is very important not only from a safety and cost viewpoint, but also for the maintenance of yield and the product quality in a process as well. Therefore, an efficient fault detection and diagnosis algorithm needs to be developed to detect faults that are present in a process and pinpoint the cause of these detected faults. Multivariate analysis technique i.e., Principal Component Analysis (PCA) and Partial Correlation analysis (PCorrA) are used to determine the correlation coefficients between the process variables and quality variables while control chart with the calculated correlation coefficients are used to facilitate the Fault Detection and Diagnosis (FDD) algorithm. A procedure for FDD has been described in this study and the proposed method is demonstrated on an Air Flow Pressure Temperature (AFPT) control system pilot plant. Results show that method based on PCA and PCorrA was able to detect the pre-designed faults successfully and identify variables which cause the faults. Asian Network for Scientific Information 2011 Article PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/24964/1/Fault%20detection%20and%20diagnosis%20for%20gas%20density%20monitoring%20using%20multivariate%20statistical%20process%20control.pdf N. S., Che Din and Noor Asma Fazli, Abdul Samad and Chin, S. Y. (2011) Fault detection and diagnosis for gas density monitoring using multivariate statistical process control. Journal of Applied Sciences, 11 (13). pp. 2400-2406. ISSN 1812-5654 http://dx.doi.org/10.3923/jas.2011.2400.2405 http://dx.doi.org/10.3923/jas.2011.2400.2405
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Malaysia Pahang
building	UMP Institutional Repository
collection	Online Access
language	English
topic	TP Chemical technology
spellingShingle	TP Chemical technology N. S., Che Din Noor Asma Fazli, Abdul Samad Chin, S. Y. Fault detection and diagnosis for gas density monitoring using multivariate statistical process control
description	Malfunction of plant equipment, instrumentation and degradation in process operation increase the operating cost of any chemical process industries. Thus, modern chemical industries need to operate as fault free as possible because faults that present in a process increase the operating cost due to the increase in waste generation and products with undesired specifications. Effective monitoring strategy for early fault detection and diagnosis is very important not only from a safety and cost viewpoint, but also for the maintenance of yield and the product quality in a process as well. Therefore, an efficient fault detection and diagnosis algorithm needs to be developed to detect faults that are present in a process and pinpoint the cause of these detected faults. Multivariate analysis technique i.e., Principal Component Analysis (PCA) and Partial Correlation analysis (PCorrA) are used to determine the correlation coefficients between the process variables and quality variables while control chart with the calculated correlation coefficients are used to facilitate the Fault Detection and Diagnosis (FDD) algorithm. A procedure for FDD has been described in this study and the proposed method is demonstrated on an Air Flow Pressure Temperature (AFPT) control system pilot plant. Results show that method based on PCA and PCorrA was able to detect the pre-designed faults successfully and identify variables which cause the faults.
format	Article
author	N. S., Che Din Noor Asma Fazli, Abdul Samad Chin, S. Y.
author_facet	N. S., Che Din Noor Asma Fazli, Abdul Samad Chin, S. Y.
author_sort	N. S., Che Din
title	Fault detection and diagnosis for gas density monitoring using multivariate statistical process control
title_short	Fault detection and diagnosis for gas density monitoring using multivariate statistical process control
title_full	Fault detection and diagnosis for gas density monitoring using multivariate statistical process control
title_fullStr	Fault detection and diagnosis for gas density monitoring using multivariate statistical process control
title_full_unstemmed	Fault detection and diagnosis for gas density monitoring using multivariate statistical process control
title_sort	fault detection and diagnosis for gas density monitoring using multivariate statistical process control
publisher	Asian Network for Scientific Information
publishDate	2011
url	http://umpir.ump.edu.my/id/eprint/24964/ http://umpir.ump.edu.my/id/eprint/24964/ http://umpir.ump.edu.my/id/eprint/24964/ http://umpir.ump.edu.my/id/eprint/24964/1/Fault%20detection%20and%20diagnosis%20for%20gas%20density%20monitoring%20using%20multivariate%20statistical%20process%20control.pdf
first_indexed	2023-09-18T22:38:04Z
last_indexed	2023-09-18T22:38:04Z
_version_	1777416729726550016

Fault detection and diagnosis for gas density monitoring using multivariate statistical process control

Similar Items