Deep residual neural network for EMI event classification using bispectrum representation

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

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

25 Downloads (Pure)

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 languageEnglish
Title of host publication2018 26th European Signal Processing Conference (EUSIPCO)
PublisherIEEE
Pages186-190
Number of pages5
ISBN (Electronic)9789082797015
DOIs
Publication statusPublished - 3 Dec 2018

Publication series

Name
ISSN (Electronic)2076-1465

    Fingerprint

Keywords

  • residual neural network
  • EMI measurement
  • electromagnetic interference
  • partial discharges
  • discharges (electric)
  • feature extraction
  • fault location
  • signal processing

Cite this

Mitiche, I., Jenkins, M. D., Boreham, P., Nesbitt, A., Stewart, B. G., & Morison, G. (2018). Deep residual neural network for EMI event classification using bispectrum representation. In 2018 26th European Signal Processing Conference (EUSIPCO) (pp. 186-190). IEEE. https://doi.org/10.23919/EUSIPCO.2018.8553177