Robust deep residual shrinkage networks for online fault classification

Alireza Salimy*, Imene Mitiche, Philip Boreham, Alan Nesbitt, Gordon Morison

*Corresponding author for this work

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

1 Citation (Scopus)
95 Downloads (Pure)


In this paper, a novel approach to improve signal classification in the presence of noise is presented. Using Stockwell transforms for feature extraction on time-series electromagnetic interference data and deep residual neural networks, containing thresholding functions (shrinkage functions) as non-linear transformation layers for classification. Thresholding functions are commonly used for signal de-noising. Setting thresholds for optimal functionality is often complex and requires expertise,this paper will investigate learned methods of threshold selection along with alternate thresholding functions. Using deep learning methods to select thresholds reduces the dependency on experts for the use of thresholding functions for de-noising and allows for adaptation to alternate noise environments. This paper proposed the novel application of two different threshold functions and introduces an architecture update for learning the threshold parameters for classification in the presence of noise. Several experiments are carried out to compare the performance of the systems with varying signal-to-noise ratio data sets taken from real-world operational high-voltage assets. Experimental results show that the proposed approaches using both Garrote and Firm thresholding achieved improved performance increases over utilizing soft thresholding within deep shrinkage networks in low signal-to-noise ratios.
Original languageEnglish
Title of host publication2021 29th European Signal Processing Conference (EUSIPCO)
Number of pages5
ISBN (Electronic)9789082797060
ISBN (Print)9781665409001
Publication statusPublished - 8 Dec 2021
Event29th European Signal Processing Conference - Online
Duration: 23 Aug 202127 Aug 2021 (Link to conference website)

Publication series

ISSN (Print)2219-5491
ISSN (Electronic)2076-1465


Conference29th European Signal Processing Conference
Abbreviated titleEUSIPCO 2021
Internet address


  • noise reduction
  • pattern classification
  • transforms
  • signal processing
  • feature extraction
  • noise measurement
  • signal denoising

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering


Dive into the research topics of 'Robust deep residual shrinkage networks for online fault classification'. Together they form a unique fingerprint.

Cite this