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Fault Detection and Diagnosis using Principal Component Analysis of Vibration Data from a Reciprocating Compressor

Ahmed, M., Gu, Fengshou and Ball, Andrew (2012) Fault Detection and Diagnosis using Principal Component Analysis of Vibration Data from a Reciprocating Compressor. In: 18th International Conference On Automation And Computing (ICAC), 2012. IEEE, Cardiff, UK, pp. 461-466. ISBN 978-1-4673-1559-3

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Abstract

This paper investigates the use of time domain vibration features for detection and diagnosis of different faults from a multi stage reciprocating compressor. Principal Component Analysis (PCA) is based to develop a detection and diagnosis framework in that the effective diagnostic features are selected from the PCA of 14 potential features and a PCA model based detection method using and statistics is subsequently developed to detect various faults including valve leakage, suction valve leakage, inter-cooler leakage, loose drive belt, discharge valve leakage combined with suction valve leakage, suction valve leakage combined with intercooler leakage and discharge valve leakage combined with intercooler leakage. Moreover a study of Q-contributions has found two original features: Histogram Lower Bound and Normal Negative log- likelihood which allow full classification of different simulated faults.

Item Type: Book Chapter
Subjects: T Technology > T Technology (General)
T Technology > TK Electrical engineering. Electronics Nuclear engineering
Schools: School of Computing and Engineering
School of Computing and Engineering > Automotive Engineering Research Group
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 > Information and Systems Engineering Group
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Depositing User: Sara Taylor
Date Deposited: 20 Sep 2012 13:41
Last Modified: 25 Aug 2015 01:30
URI: http://eprints.hud.ac.uk/id/eprint/15004

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