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

B. S. Payne, F. Gu, C. J.S. Webber, A. D. Ball

Research output: Contribution to journalArticle

1 Citation (Scopus)

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.

LanguageEnglish
Pages901-915
Number of pages15
JournalProceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science
Volume217
Issue number8
DOIs
Publication statusPublished - 1 Aug 2003
Externally publishedYes

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Signal reconstruction
Machine components
Condition monitoring
Benchmarking
Induction motors
Principal component analysis
Fluxes
Neural networks

Cite this

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title = "Componential coding in the condition monitoring of electrical machines: Part 2: Application to a conventional machine and a novel machine",
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.",
keywords = "Auto-encoder, Componential coding, Condition monitoring, Fault detection, Fault diagnosis, Induction motor, Neural network, Transverse flux motor",
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