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Related Experiment Video

Updated: Feb 5, 2026

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Imaging Time Series for the Classification of EMI Discharge Sources.

Imene Mitiche1, Gordon Morison2, Alan Nesbitt3

  • 1Department of Engineering, Glasgow Caledonian University, 70 Cowcaddens Road, Glasgow G4 0BA, UK. imene.mitiche@gcu.ac.uk.

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|September 19, 2018
PubMed
Summary
This summary is machine-generated.

New image processing techniques improve Electromagnetic Interference (EMI) source classification from power plants. Local Binary Pattern (LBP) and Local Phase Quantisation (LPQ) offer superior accuracy for identifying EMI discharge types.

Keywords:
EMI discharge sourcesEMI methodGramian Angular FieldLocal Binary PatternLocal Phase Quantisationclassification

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Area of Science:

  • Electrical Engineering
  • Signal Processing
  • Power Systems

Background:

  • Electromagnetic Interference (EMI) is a significant challenge in power plant operations.
  • Accurate classification of EMI sources is crucial for effective mitigation strategies.
  • Existing methods struggle with the complexity of new power plant EMI data.

Purpose of the Study:

  • To develop and evaluate advanced feature extraction and dimension reduction techniques for EMI source classification.
  • To enhance the accuracy and robustness of classifying diverse EMI discharge sources.
  • To address the challenges posed by a complex and extensive EMI dataset from new power plant sites.

Main Methods:

  • Utilized the Gramian Angular Field (GAF) technique to transform EMI time signals into image representations.
  • Applied Local Binary Pattern (LBP) and Local Phase Quantisation (LPQ) for feature extraction from GAF images.
  • Implemented a Random Forest (RF) classifier to categorize EMI discharge sources based on extracted features.
  • Compared the performance of GAF-LBP and GAF-LPQ against a previous method using accuracy, precision, recall, and F-measure.

Main Results:

  • The proposed GAF-LBP and GAF-LPQ methods demonstrated significantly higher classification performance compared to the previous approach.
  • The GAF-LBP method achieved the best overall results, indicating its effectiveness in capturing unique EMI discharge fingerprints.
  • Feature vectors derived from the mapped images effectively represented the distinct characteristics of different EMI sources.

Conclusions:

  • Gramian Angular Field combined with LBP and LPQ offers a powerful approach for complex EMI source classification.
  • The developed methods provide a more accurate and reliable solution for identifying EMI discharge types in power plants.
  • This study advances the field of EMI analysis through innovative image processing and machine learning techniques.