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

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

Electronic And Electrical Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution book

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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.

Original language	English
Title of host publication	26th European Signal Processing Conference (EUSIPCO)
Place of Publication	Piscataway, NJ
Publisher	IEEE
Number of pages	5
Publication status	Accepted/In press - 21 Jun 2018

Keywords

condition monitoring
power plant
EMI

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Mitiche-etal-EUSIPCO2018-Deep-residual-neural-network-for-EMI-event-classificationAccepted author manuscript, 1.88 MB

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@inproceedings{39564ded0d9842deab161d7082a0093a,

title = "Deep residual neural network for EMI event classification using bispectrum representations",

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.",

keywords = "condition monitoring, power plant, EMI",

author = "Imene Mitiche and Jenkins, {Mark David} and Philip Boreham and Alan Nesbitt and Stewart, {Brian G.} and Gordon Morison",

note = "{\textcopyright} 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.",

year = "2018",

month = jun,

day = "21",

language = "English",

booktitle = "26th European Signal Processing Conference (EUSIPCO)",

publisher = "IEEE",

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TY - GEN

T1 - Deep residual neural network for EMI event classification using bispectrum representations

AU - Mitiche, Imene

AU - Jenkins, Mark David

AU - Boreham, Philip

AU - Nesbitt, Alan

AU - Stewart, Brian G.

AU - Morison, Gordon

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.

PY - 2018/6/21

Y1 - 2018/6/21

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.

AB - 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.

KW - condition monitoring

KW - power plant

KW - EMI

UR - http://www.eusipco2018.org/

M3 - Conference contribution book

BT - 26th European Signal Processing Conference (EUSIPCO)

PB - IEEE

CY - Piscataway, NJ

ER -

Deep residual neural network for EMI event classification using bispectrum representations

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