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Fault Diagnosis of Reciprocating Compressors Using Revelance Vector Machines with A Genetic Algorithm Based on Vibration Data

Ahmed, M., Smith, Ann, Gu, Fengshou and Ball, Andrew (2014) Fault Diagnosis of Reciprocating Compressors Using Revelance Vector Machines with A Genetic Algorithm Based on Vibration Data. In: The 20th International Conference on Automation and Computing (ICAC'14), 12-13 September 2014, Cranfield University. (Unpublished)

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Abstract

This paper focuses on the development of an advanced fault classifier for monitoring reciprocating compressors (RC) based on vibration signals. Many feature parameters can be used for fault diagnosis, here the classifier is developed based on a relevance vector machine (RVM) which is optimized with genetic algorithms (GA) so determining a more effective subset of the parameters. Both a one-against-one scheme based RVM and a multiclass multi-kernel relevance vector machine (mRVM) have been evaluated to identify a more effective method for implementing the multiclass fault classification for the compressor. The accuracy of both techniques is discussed correspondingly to determine an optimal fault classifier which can correlate with the physical mechanisms underlying the features. The results show that the models perform well, the classification accuracy rate being up to 97% for both algorithms.

Item Type: Conference or Workshop Item (Paper)
Subjects: 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
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Depositing User: Cherry Edmunds
Date Deposited: 22 Oct 2014 13:29
Last Modified: 05 Nov 2015 02:00
URI: http://eprints.hud.ac.uk/id/eprint/22134

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