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

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Mitiche, Imene and Nesbitt, Alan and Boreham, Philip and Stewart, Brian G. and Morison, Gordon; (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, IDN, pp. 162-166. ISBN 9781538673584 (https://doi.org/10.1109/IOTAIS.2018.8600914)

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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.

  • Item type:Book Section
    ID code:70791
    Dates:
    Date
    Event
    7 January 2019
    Published
    30 August 2018
    Accepted
    Notes:© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
    Subjects:Technology > Electrical engineering. Electronics Nuclear engineering
    Department:Faculty of Engineering > Electronic and Electrical Engineering
    Depositing user: Pure Administrator
    Date deposited:11 Dec 2019 12:14
    Last modified:14 Dec 2024 01:06
    URI:https://strathprints.strath.ac.uk/id/eprint/70791
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