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Bispectrum Analysis of Motor Current Signals for Fault Diagnosis of Reciprocating Compressors

Naid, A., Gu, Fengshou, Shao, Yimin, Al-Arbi, Salem and Ball, Andrew (2009) Bispectrum Analysis of Motor Current Signals for Fault Diagnosis of Reciprocating Compressors. Key Engineering Materials, 413-41. pp. 505-511. ISSN 1013-9826

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Abstract

The induction motor is the most common driver in industry and has been previously
proposed as a means of inferring the condition of an entire equipment train, predominantly through
the measurement and processing of power supply parameters. This has obvious advantages in terms
of being non-intrusive or remote, less costly to apply and improved safety. This paper describes the
use of the induction motor current to identify and quantify a number of common faults seeded on a
two-stage reciprocating compressor. An analysis of the compressor working cycle leads to current
signal the components that are sensitive to the common faults seeded to compressor system, and
second- and third-order signal processing tools are used to analyse the current signals. It is shown
that the developed diagnostic features: the bispectral peak value from the amplitude modulation
bispectrum and the kurtosis from the current gives rise to reliable fault classification results. The
low feature values can differentiate the belt looseness from other fault cases and valve leakage and
inter-cooler leakage can be separated easily using two linear classifiers. This work provides a novel
approach to the analysis stator current data for the diagnosis of motor drive faults.

Item Type: Article
Additional Information: (c) Trans Tech Publications
Uncontrolled Keywords: Reciprocating compressor, Bispectrum, Kurtosis, Motor Current Signature Analysis
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: Graham Stone
Date Deposited: 27 May 2009 15:54
Last Modified: 19 Aug 2015 23:03
URI: http://eprints.hud.ac.uk/id/eprint/4536

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