Abstract
| Original language | English |
|---|---|
| Title of host publication | European Signal Processing Conference Proceedings |
| Publisher | IEEE |
| Number of pages | 5 |
| Publication status | Accepted/In press - 3 Jun 2019 |
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Deep complex neural network learning for high-voltage insulation fault classification from complex bispectrum representation. / Mitiche, Imene; Jenkins, Mark; Boreham, Philip ; Nesbitt, Alan; Morison, Gordon.
European Signal Processing Conference Proceedings. IEEE, 2019.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
TY - GEN
T1 - Deep complex neural network learning for high-voltage insulation fault classification from complex bispectrum representation
AU - Mitiche, Imene
AU - Jenkins, Mark
AU - Boreham, Philip
AU - Nesbitt, Alan
AU - Morison, Gordon
N1 - Acceptance in SAN AAM: no embargo
PY - 2019/6/3
Y1 - 2019/6/3
N2 - Bispectrum representations previously achieved a successful classification of insulation fault signals in High-Voltage (HV) power plant. The magnitude information of the Bispectrum was implemented as a feature for a Deep Neural Network. This preliminary research brought interest in evaluating the performance of Bispectrum as complex input features that are implemented into a Deep Complex Valued Convolutional Neural Network (CV-CNN). This paper presents the application of this novel method to condition monitoring of High Voltage (HV) power plant equipment. Discharge signals related to HV insulation faults are measured in a real-world power plant using the Electromagnetic Interference (EMI) method and processed using third order Higher-Order Statistics (HOS) to obtain a Bispectrum representation. By mapping the time-domain signal to Bispectrum representations the problem can be approached as a complex-valued classification task. This allows for the novel combination of complex Bispectrum and CV-CNN applied to the classification of HV discharge signals. The network is trained on signals from 9 classes and achieves high classification accuracy in each category, improving upon the performance of a Real Valued CNN (RV-CNN).
AB - Bispectrum representations previously achieved a successful classification of insulation fault signals in High-Voltage (HV) power plant. The magnitude information of the Bispectrum was implemented as a feature for a Deep Neural Network. This preliminary research brought interest in evaluating the performance of Bispectrum as complex input features that are implemented into a Deep Complex Valued Convolutional Neural Network (CV-CNN). This paper presents the application of this novel method to condition monitoring of High Voltage (HV) power plant equipment. Discharge signals related to HV insulation faults are measured in a real-world power plant using the Electromagnetic Interference (EMI) method and processed using third order Higher-Order Statistics (HOS) to obtain a Bispectrum representation. By mapping the time-domain signal to Bispectrum representations the problem can be approached as a complex-valued classification task. This allows for the novel combination of complex Bispectrum and CV-CNN applied to the classification of HV discharge signals. The network is trained on signals from 9 classes and achieves high classification accuracy in each category, improving upon the performance of a Real Valued CNN (RV-CNN).
M3 - Conference contribution
BT - European Signal Processing Conference Proceedings
PB - IEEE
ER -