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

8 Citations (Scopus)
113 Downloads (Pure)


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)
Number of pages5
ISBN (Electronic)9781538673584
Publication statusPublished - 7 Jan 2019


  • high voltage
  • condition monitoring


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