Abstract
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 language | English |
|---|---|
| Title of host publication | 2018 IEEE International Conference on Internet of Things and Intelligence System (IOTAIS) |
| Publisher | IEEE |
| Pages | 162-166 |
| Number of pages | 5 |
| ISBN (Electronic) | 9781538673584 |
| DOIs | |
| Publication status | Published - 7 Jan 2019 |
Fingerprint
Keywords
- high voltage
- condition monitoring
Cite this
}
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 proceeding › Conference contribution
TY - GEN
T1 - Naive bayes multi-label classification approach for high-voltage condition monitoring
AU - Mitiche, Imene
AU - Nesbitt, Alan
AU - Boreham, Philip
AU - Stewart, Brian G.
AU - Morison, Gordon
N1 - Acceptance in SAN AAM: no embargo No ISSN found for output, out of policy scope. ST 13/11/19
PY - 2019/1/7
Y1 - 2019/1/7
N2 - 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.
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.
KW - high voltage
KW - condition monitoring
U2 - 10.1109/IOTAIS.2018.8600914
DO - 10.1109/IOTAIS.2018.8600914
M3 - Conference contribution
SP - 162
EP - 166
BT - 2018 IEEE International Conference on Internet of Things and Intelligence System (IOTAIS)
PB - IEEE
ER -