Leak detection of pipeline: An integrated approach of rough set theory and artificial bee colony trained SVM

Santosh Kumar Mandal, Tung Sun Chan, M. K. Tiwari

Research output: Journal article publicationJournal articleAcademic researchpeer-review

85 Citations (Scopus)

Abstract

The generation of leak along the pipeline carrying crude oils and liquid fuels results enormous financial loss to the industry and also affects the public health. Hence, the leak detection and localization problem has always been a major concern for the companies. In spite of the various techniques developed, accuracy and time involved in the prediction is still a matter of concern. In this paper, a novel leak detection scheme based on rough set theory and support vector machine (SVM) is proposed to overcome the problem of false leak detection. In this approach, 'rough set theory' is explored to reduce the length of experimental data as well as generate rules. It is embedded to enhance the decision making process. Further, SVM classifier is employed to inspect the cases that could not be detected by applied rules. For the computational training of SVM, this paper uses swarm intelligence technique: artificial bee colony (ABC) algorithm, which imitates intelligent food searching behavior of honey bees. The results of proposed leak detection scheme with ABC are compared with those obtained by using particle swarm optimization (PSO) and one of its variants, so-called enhanced particle swarm optimization (EPSO). The experimental results advocate the use of propounded method for detecting leaks with maximum accuracy.
Original languageEnglish
Pages (from-to)3071-3080
Number of pages10
JournalExpert Systems with Applications
Volume39
Issue number3
DOIs
Publication statusPublished - 15 Feb 2012

Keywords

  • Artificial bee colony (ABC) algorithm
  • Leak detection
  • Rough set theory
  • Rule generation
  • Support vector machine (SVM)

ASJC Scopus subject areas

  • Engineering(all)
  • Computer Science Applications
  • Artificial Intelligence

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