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Sensors 2018, 18(9), 3098; https://doi.org/10.3390/s18093098

Imaging Time Series for the Classification of EMI Discharge Sources

1
Department of Engineering, Glasgow Caledonian University, 70 Cowcaddens Road, Glasgow G4 0BA, UK
2
Institute of Energy and Environment, University of Strathclyde, 204 George Street, Glasgow G1 1XW, UK
3
Innovation Centre for Online Systems, 7 Townsend Business Park, Bere Regis BH20 7LA, UK
*
Author to whom correspondence should be addressed.
Received: 26 July 2018 / Revised: 11 September 2018 / Accepted: 12 September 2018 / Published: 14 September 2018
(This article belongs to the Special Issue UHF and RF Sensor Technology for Partial Discharge Detection)
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Abstract

In this work, we aim to classify a wider range of Electromagnetic Interference (EMI) discharge sources collected from new power plant sites across multiple assets. This engenders a more complex and challenging classification task. The study involves an investigation and development of new and improved feature extraction and data dimension reduction algorithms based on image processing techniques. The approach is to exploit the Gramian Angular Field technique to map the measured EMI time signals to an image, from which the significant information is extracted while removing redundancy. The image of each discharge type contains a unique fingerprint. Two feature reduction methods called the Local Binary Pattern (LBP) and the Local Phase Quantisation (LPQ) are then used within the mapped images. This provides feature vectors that can be implemented into a Random Forest (RF) classifier. The performance of a previous and the two new proposed methods, on the new database set, is compared in terms of classification accuracy, precision, recall, and F-measure. Results show that the new methods have a higher performance than the previous one, where LBP features achieve the best outcome. View Full-Text
Keywords: EMI method; EMI discharge sources; classification; Gramian Angular Field; Local Binary Pattern; Local Phase Quantisation EMI method; EMI discharge sources; classification; Gramian Angular Field; Local Binary Pattern; Local Phase Quantisation
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Mitiche, I.; Morison, G.; Nesbitt, A.; Hughes-Narborough, M.; Stewart, B.G.; Boreham, P. Imaging Time Series for the Classification of EMI Discharge Sources. Sensors 2018, 18, 3098.

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