Naive bayes multi-label classification approach for high-voltage condition monitoring

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

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

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Abstract

This paper addresses for the first time the multilabel classification of High-Voltage (HV) discharges captured using the Electromagnetic Interference (EMI) method for HV machines. The approach involves feature extraction from EMI
time signals, emitted during the discharge events, by means of 1D-Local Binary Pattern (LBP) and 1D-Histogram of Oriented Gradients (HOG) techniques. Their combination provides a feature vector that is implemented in a naive Bayes classifier designed to identify the labels of two or more discharge sources
contained within a single signal. The performance of this novel approach is measured using various metrics including average precision, accuracy, specificity, hamming loss etc. Results demonstrate a successful performance that is in line with similar application to other fields such as biology and image processing. This first attempt of multi-label classification of EMI discharge
sources opens a new research topic in HV condition monitoring.
Original languageEnglish
Title of host publication2018 IEEE International Conference on Internet of Things and Intelligence System (IOTAIS)
PublisherIEEE
Pages162-166
Number of pages5
ISBN (Electronic)9781538673584
DOIs
Publication statusPublished - 7 Jan 2019

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Condition monitoring
Labels
Signal interference
Electric potential
Feature extraction
Image processing
Classifiers

Keywords

  • high voltage
  • condition monitoring

Cite this

Mitiche, I., Nesbitt, A., Boreham, P., Stewart, B. G., & Morison, G. (2019). Naive bayes multi-label classification approach for high-voltage condition monitoring. In 2018 IEEE International Conference on Internet of Things and Intelligence System (IOTAIS) (pp. 162-166). IEEE. https://doi.org/10.1109/IOTAIS.2018.8600914
Mitiche, Imene ; Nesbitt, Alan ; Boreham, Philip ; Stewart, Brian G. ; Morison, Gordon. / Naive bayes multi-label classification approach for high-voltage condition monitoring. 2018 IEEE International Conference on Internet of Things and Intelligence System (IOTAIS). IEEE, 2019. pp. 162-166
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title = "Naive bayes multi-label classification approach for high-voltage condition monitoring",
abstract = "This paper addresses for the first time the multilabel classification of High-Voltage (HV) discharges captured using the Electromagnetic Interference (EMI) method for HV machines. The approach involves feature extraction from EMItime signals, emitted during the discharge events, by means of 1D-Local Binary Pattern (LBP) and 1D-Histogram of Oriented Gradients (HOG) techniques. Their combination provides a feature vector that is implemented in a naive Bayes classifier designed to identify the labels of two or more discharge sourcescontained within a single signal. The performance of this novel approach is measured using various metrics including average precision, accuracy, specificity, hamming loss etc. Results demonstrate a successful performance that is in line with similar application to other fields such as biology and image processing. This first attempt of multi-label classification of EMI dischargesources opens a new research topic in HV condition monitoring.",
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author = "Imene Mitiche and Alan Nesbitt and Philip Boreham and Stewart, {Brian G.} and Gordon Morison",
note = "Acceptance in SAN AAM: no embargo No ISSN found for output, out of policy scope. ST 13/11/19",
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Mitiche, I, Nesbitt, A, Boreham, P, Stewart, BG & Morison, G 2019, Naive bayes multi-label classification approach for high-voltage condition monitoring. in 2018 IEEE International Conference on Internet of Things and Intelligence System (IOTAIS). IEEE, pp. 162-166. https://doi.org/10.1109/IOTAIS.2018.8600914

Naive bayes multi-label classification approach for high-voltage condition monitoring. / Mitiche, Imene; Nesbitt, Alan; Boreham, Philip ; Stewart, Brian G.; Morison, Gordon.

2018 IEEE International Conference on Internet of Things and Intelligence System (IOTAIS). IEEE, 2019. p. 162-166.

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

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AB - This paper addresses for the first time the multilabel classification of High-Voltage (HV) discharges captured using the Electromagnetic Interference (EMI) method for HV machines. The approach involves feature extraction from EMItime signals, emitted during the discharge events, by means of 1D-Local Binary Pattern (LBP) and 1D-Histogram of Oriented Gradients (HOG) techniques. Their combination provides a feature vector that is implemented in a naive Bayes classifier designed to identify the labels of two or more discharge sourcescontained within a single signal. The performance of this novel approach is measured using various metrics including average precision, accuracy, specificity, hamming loss etc. Results demonstrate a successful performance that is in line with similar application to other fields such as biology and image processing. This first attempt of multi-label classification of EMI dischargesources opens a new research topic in HV condition monitoring.

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Mitiche I, Nesbitt A, Boreham P, Stewart BG, Morison G. Naive bayes multi-label classification approach for high-voltage condition monitoring. In 2018 IEEE International Conference on Internet of Things and Intelligence System (IOTAIS). IEEE. 2019. p. 162-166 https://doi.org/10.1109/IOTAIS.2018.8600914