NDLI: Efficient faulty variable selection and parsimonious reconstruction modeling for fault diagnosis

Content Provider	IEEE Xplore Digital Library
Author	Wang Wei Zhao Chunhui
Copyright Year	2015
Description	Author affiliation: Dept. of Control Sci. & Eng., China Tobacco Zhejiang Ind. Co. Ltd., Hangzhou, China (Wang Wei; Zhao Chunhui)
Abstract	In the present work, an efficient faulty variable selection algorithm is proposed by developing iterative selection procedure. The significant faulty variables that cover the most fault effects and thus highly contribute to alarm monitoring statistics are distinguished from those general variables that are deemed to follow normal rules and thus uninformative to reveal fault effects. Then to further reveal the fault characteristics, the selected significant faulty variables are chosen to get parsimonious reconstruction model for fault diagnosis where relative analysis is performed on these selected faulty variables to find the relative changes from normal to fault. The faulty variable selection can not only pick up those responsible variables but also exclude the influences of uninformative variables and thus more effectively explore fault effects. It can also help to find more interesting and reliable model representation, better identify the underlying fault information and get enhanced process understanding. Its feasibility and performance are illustrated with simulated faults using data from the Tennessee Eastman (TE) benchmark process.
Starting Page	6236
Ending Page	6241
File Size	254255
Page Count	6
File Format	PDF
ISBN	9789881563897
ISSN	19341768
DOI	10.1109/ChiCC.2015.7260618
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-07-28
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Technical Committee on Control Theory, Chinese Association of Automation
Subject Keyword	Decision support systems TV Efficient Faulty Variable Parsimonious Reconstruction Model Fault Diagnosis Iterative Selection
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Locating faulty variables by evaluating ratio of variable contribution based on discriminant analysis for online fault diagnosis

Fault Relevant Variable Selection for Fault Diagnosis

Fault subspace selection and analysis of relative changes based reconstruction modeling for multi-fault diagnosis

Adaptive fault diagnosis for continuous time-delay repetitive system subject to sensor fault

Fault reconstruction algorithm based on fault-relevant KPCA

Application of intelligent fault diagnosis technology in HVDC system

A novel fault reconstruction scheme for a class of nonlinear systems

Knowledge and model based decision support for power system protection engineers

Fault diagnosis for finite state systems.

Efficient faulty variable selection and parsimonious reconstruction modeling for fault diagnosis

Similar Documents

Locating faulty variables by evaluating ratio of variable contribution based on discriminant analysis for online fault diagnosis

Fault Relevant Variable Selection for Fault Diagnosis

Fault subspace selection and analysis of relative changes based reconstruction modeling for multi-fault diagnosis

Adaptive fault diagnosis for continuous time-delay repetitive system subject to sensor fault

Fault reconstruction algorithm based on fault-relevant KPCA

Application of intelligent fault diagnosis technology in HVDC system

A novel fault reconstruction scheme for a class of nonlinear systems

Knowledge and model based decision support for power system protection engineers

Fault diagnosis for finite state systems.

Efficient faulty variable selection and parsimonious reconstruction modeling for fault diagnosis