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Data Clustering Approach to Industrial Process Monitoring, Fault Detection and Isolation

by Kiran Jyoti, Satyaveer Singh
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 17 - Number 2
Year of Publication: 2011
Authors: Kiran Jyoti, Satyaveer Singh
10.5120/2189-2777

Kiran Jyoti, Satyaveer Singh . Data Clustering Approach to Industrial Process Monitoring, Fault Detection and Isolation. International Journal of Computer Applications. 17, 2 ( March 2011), 41-45. DOI=10.5120/2189-2777

@article{ 10.5120/2189-2777,
author = { Kiran Jyoti, Satyaveer Singh },
title = { Data Clustering Approach to Industrial Process Monitoring, Fault Detection and Isolation },
journal = { International Journal of Computer Applications },
issue_date = { March 2011 },
volume = { 17 },
number = { 2 },
month = { March },
year = { 2011 },
issn = { 0975-8887 },
pages = { 41-45 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume17/number2/2189-2777/ },
doi = { 10.5120/2189-2777 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:04:37.476472+05:30
%A Kiran Jyoti
%A Satyaveer Singh
%T Data Clustering Approach to Industrial Process Monitoring, Fault Detection and Isolation
%J International Journal of Computer Applications
%@ 0975-8887
%V 17
%N 2
%P 41-45
%D 2011
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In this paper proposes different conventional and fuzzy based clustering techniques for fault detection and isolation in process plant monitoring. Process plant monitoring is very important aspect to improve productiveness and efficiency of the product and plant. This paper takes a case study of plant data and implements K means algorithm and fuzzy C means algorithm to cluster the relevant data. This paper also discusses the comparison for K means algorithm and fuzzy C means algorithm.

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Index Terms

Computer Science
Information Sciences

Keywords

Conventional Clustering Fuzzy Based Clustering Fault Detection and Isolation

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