Deep residual neural network for EMI event classification using bispectrum representations

Imene Mitiche, Mark David Jenkins, Philip Boreham, Alan Nesbitt, Brian G. Stewart, Gordon Morison

Research output: Chapter in Book/Report/Conference proceedingConference contribution book

Abstract

This paper presents a novel method for condition monitoring of High Voltage (HV) power plant equipment through analysis of discharge signals. These discharge signals are measured using the Electromagnetic Interference (EMI) method and processed using third order Higher-Order Statistics (HOS) to obtain a Bispectrum representation. By mapping the time-domain signal to a Bispectrum image representations the problem can be approached as an image classification task. This allows for the novel application of a Deep Residual Neural Network (ResNet) to the classification of HV discharge signals. The network is trained on signals into 9 classes and achieves high classification accuracy in each category, improving upon our previous work on this task.
LanguageEnglish
Title of host publication26th European Signal Processing Conference (EUSIPCO)
Place of PublicationPiscataway, NJ
PublisherIEEE
Number of pages5
Publication statusAccepted/In press - 21 Jun 2018

Fingerprint

Signal interference
Neural networks
Higher order statistics
Image classification
Condition monitoring
Electric potential
Power plants

Keywords

  • condition monitoring
  • power plant
  • EMI

Cite this

Mitiche, I., Jenkins, M. D., Boreham, P., Nesbitt, A., Stewart, B. G., & Morison, G. (Accepted/In press). Deep residual neural network for EMI event classification using bispectrum representations. In 26th European Signal Processing Conference (EUSIPCO) Piscataway, NJ: IEEE.
Mitiche, Imene ; Jenkins, Mark David ; Boreham, Philip ; Nesbitt, Alan ; Stewart, Brian G. ; Morison, Gordon. / Deep residual neural network for EMI event classification using bispectrum representations. 26th European Signal Processing Conference (EUSIPCO). Piscataway, NJ : IEEE, 2018.
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abstract = "This paper presents a novel method for condition monitoring of High Voltage (HV) power plant equipment through analysis of discharge signals. These discharge signals are measured using the Electromagnetic Interference (EMI) method and processed using third order Higher-Order Statistics (HOS) to obtain a Bispectrum representation. By mapping the time-domain signal to a Bispectrum image representations the problem can be approached as an image classification task. This allows for the novel application of a Deep Residual Neural Network (ResNet) to the classification of HV discharge signals. The network is trained on signals into 9 classes and achieves high classification accuracy in each category, improving upon our previous work on this task.",
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Mitiche, I, Jenkins, MD, Boreham, P, Nesbitt, A, Stewart, BG & Morison, G 2018, Deep residual neural network for EMI event classification using bispectrum representations. in 26th European Signal Processing Conference (EUSIPCO). IEEE, Piscataway, NJ.

Deep residual neural network for EMI event classification using bispectrum representations. / Mitiche, Imene; Jenkins, Mark David; Boreham, Philip; Nesbitt, Alan; Stewart, Brian G.; Morison, Gordon.

26th European Signal Processing Conference (EUSIPCO). Piscataway, NJ : IEEE, 2018.

Research output: Chapter in Book/Report/Conference proceedingConference contribution book

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N1 - © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

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N2 - This paper presents a novel method for condition monitoring of High Voltage (HV) power plant equipment through analysis of discharge signals. These discharge signals are measured using the Electromagnetic Interference (EMI) method and processed using third order Higher-Order Statistics (HOS) to obtain a Bispectrum representation. By mapping the time-domain signal to a Bispectrum image representations the problem can be approached as an image classification task. This allows for the novel application of a Deep Residual Neural Network (ResNet) to the classification of HV discharge signals. The network is trained on signals into 9 classes and achieves high classification accuracy in each category, improving upon our previous work on this task.

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Mitiche I, Jenkins MD, Boreham P, Nesbitt A, Stewart BG, Morison G. Deep residual neural network for EMI event classification using bispectrum representations. In 26th European Signal Processing Conference (EUSIPCO). Piscataway, NJ: IEEE. 2018