Efficient fault detection using support vector machine based hybrid expert system

Buddha Kishore (), M. R. S. Satyanarayana and K. Sujatha
Additional contact information
Buddha Kishore: GITAM University
M. R. S. Satyanarayana: GITAM University
K. Sujatha: Miracle Engineering College

International Journal of System Assurance Engineering and Management, 2016, vol. 7, issue 1, No 5, 34-40

Abstract: Abstract This paper demonstrates the methodology of fault classification of rotating machinery using support vector machine (SVM) in combination with genetic algorithm and particle swarm optimization. In order to detect the machine health condition, classifier uses the features as the inputs from the preprocessed raw signal of a machine. Support vector machine classifier prepared in combination of hybrid adaptive particle swarm optimization and adaptive genetic algorithm (HAPAG) proposed for proficient flaw detection. An industrial case study of a centrifugal pump is considered and the data is given for both training and testing of the classifier. A similar study with comparable existing fault classifiers on the identification triumph is investigated. SVM based HAPAG system results in clustering the various faults with more than 90 % accuracy when compared with adaptive tuning of SVM based techniques like SVM—adaptive particle swarm optimization and SVM—adaptive genetic algorithm. The outcome indicates the adequacy of choosing the classifiers in finding the machine health condition.

Keywords: Adaptive tuning; Fault detection; Support vector machine; Genetic algorithm; Hybrid adaptive particle swarm optimization and adaptive genetic algorithm (search for similar items in EconPapers)
Date: 2016
References: View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
http://link.springer.com/10.1007/s13198-014-0281-y Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:ijsaem:v:7:y:2016:i:1:d:10.1007_s13198-014-0281-y

Ordering information: This journal article can be ordered from
http://www.springer.com/engineering/journal/13198

DOI: 10.1007/s13198-014-0281-y

Access Statistics for this article

International Journal of System Assurance Engineering and Management is currently edited by P.K. Kapur, A.K. Verma and U. Kumar

More articles in International Journal of System Assurance Engineering and Management from Springer, The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().