Search:
Computing and Library Services - delivering an inspiring information environment

Componential coding in the condition monitoring of electrical machines Part 2: application to a conventional machine and a novel machine

Payne, B.S., Gu, Fengshou, Webber, C J S and Ball, Andrew (2003) Componential coding in the condition monitoring of electrical machines Part 2: application to a conventional machine and a novel machine. Proceedings of the Institute of Mechanical Engineering Part C, Journal of Mechanical Engineering Science, 217 (8). pp. 901-915. ISSN 0954-4062

[img]
Preview
PDF - Published Version
Download (4MB) | Preview

Abstract

This paper (Part 2) presents the practical application of componential coding, the principles of which were described in the accompanying Part 1 paper. Four major issues are addressed, including optimization of the neural network, assessment of the anomaly detection results, development of diagnostic approaches (based on the reconstruction error) and also benchmarking of componential coding with other techniques (including waveform measures, Fourier-based signal reconstruction and principal component analysis). This is achieved by applying componential coding to the data monitored from both a conventional induction motor and from a novel transverse flux motor. The results reveal that machine condition monitoring using componential coding is not only capable of detecting and then diagnosing anomalies but it also outperforms other conventional techniques in that it is able to separate very small and localized anomalies.

Item Type: Article
Subjects: T Technology > T Technology (General)
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: Sharon Beastall
Date Deposited: 20 Jan 2010 14:04
Last Modified: 24 Aug 2015 05:08
URI: http://eprints.hud.ac.uk/id/eprint/6810

Downloads

Downloads per month over past year

Repository Staff Only: item control page

View Item View Item

University of Huddersfield, Queensgate, Huddersfield, HD1 3DH Copyright and Disclaimer All rights reserved ©