Deep residual neural network for EMI event classification using bispectrum representations

Mitiche, Imene and Jenkins, Mark David and Boreham, Philip and Nesbitt, Alan and Stewart, Brian G. and Morison, Gordon; (2018) Deep residual neural network for EMI event classification using bispectrum representations. In: 26th European Signal Processing Conference (EUSIPCO). IEEE, Piscataway, NJ. (In Press)

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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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  Item type:	Book Section
  ID code:	64738
  Dates:	Date Event 21 June 2018 Published 21 June 2018 Accepted
  Notes:	© 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.
  Subjects:	Technology > Electrical engineering. Electronics Nuclear engineering
  Department:	Faculty of Engineering > Electronic and Electrical Engineering
  Depositing user:	Pure Administrator
  Date deposited:	09 Jul 2018 13:47
  Last modified:	16 Nov 2024 01:31
  Related URLs:	Event
  URI:	https://strathprints.strath.ac.uk/id/eprint/64738