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Fault Classification of Reciprocating Compressor Based on Neural Networks and Support Vector Machines

Ahmed, Mahmud, Abdusslam, S.A., Baqqar, Mabrouka, Gu, Fengshou and Ball, Andrew (2011) Fault Classification of Reciprocating Compressor Based on Neural Networks and Support Vector Machines. In: Proceedings of the 17th International Conference on Automation & Computing. Chinese Automation and Computing Society, Huddersfield. ISBN 978-1-86218-098-7

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Abstract

Reciprocating compressors play a major part in
many industrial systems and faults occurring in them can degrade performance, consume additional energy, cause severe damage to the machine and possibly even system shut-down. Traditional vibration monitoring techniques have found it difficult to determine a set of effective diagnostic features due to the high complexity of the vibration signals because of the many different impact sources and wide range of practical operating conditions.

This paper focuses on the development of an advanced signal classifier for a reciprocating compressor using vibration signals. Artificial Neural Networks (ANN) and Support Vector Machines (SVM) have been applied, trained and tested for feature extraction and fault classification.
The accuracy of both techniques is compared to
determine the optimum fault classifier. The results show that the model behaves well, and classification rate accuracy is up to 100% for both binary classes (a single fault present in the compressor) and multi-classes (three faults present).

Item Type: Book Chapter
Uncontrolled Keywords: Fault Diagnosis, Reciprocating Compressor, Artificial Neural Networks, Support Vector Machine
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
T Technology > TJ Mechanical engineering and machinery
Schools: School of Computing and Engineering
School of Computing and Engineering > Automotive Engineering Research Group
School of Computing and Engineering > Diagnostic Engineering Research Centre
School of Computing and Engineering > Diagnostic Engineering Research Centre > Energy, Emissions and the Environment Research Group
School of Computing and Engineering > Diagnostic Engineering Research Centre > Machinery Condition and Performance Monitoring Research Group
School of Computing and Engineering > Diagnostic Engineering Research Centre > Measurement System and Signal Processing Research Group
School of Computing and Engineering > High-Performance Intelligent Computing
School of Computing and Engineering > High-Performance Intelligent Computing > Information and Systems Engineering Group
Related URLs:
Depositing User: Cherry Edmunds
Date Deposited: 16 Sep 2011 12:58
Last Modified: 09 Aug 2012 15:21
URI: http://eprints.hud.ac.uk/id/eprint/11491

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