Feature Extraction and Classification for Detecting the Thermal Faults in Electrical Installations

M. S., Jadin and Kamarul Hawari, Ghazali and Soib, Taib (2014) Feature Extraction and Classification for Detecting the Thermal Faults in Electrical Installations. Measurement, 57. pp. 15-24. ISSN 0263-2241. (Published)

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This paper proposed an effort to investigate the suitability of input features and classifier for identifying thermal faults within electrical installations. The features are extracted from the thermal images of electrical equipment and classified using a multilayered perceptron (MLP) artificial neural network and support vector machine (SVM). In the experiments, the classification performances from various input features are evaluated. The commonly used classification performance indices, including sensitivity, specificity, accuracy, area under curve (AUC), receiver operating characteristic (ROC) and F-score are employed to identify the most suitable input feature as well as the best configuration of classifiers. The experimental results demonstrate that the combination of features set Tmax, Tdelta and DTbg produce the best input feature for thermal fault detection. In addition, the implementation of SVM using radial basis kernel function (RBF) produces slightly better performance than the MLP artificial neural network.

Item Type: Article
Additional Information: Indexed by Scopus. IF: 1.526
Uncontrolled Keywords: Infrared image; Feature extraction; Electrical installations; Classification; Qualitative evaluation
Subjects: T Technology > TK Electrical engineering. Electronics Nuclear engineering
Faculty/Division: Faculty of Electrical & Electronic Engineering
Depositing User: Mrs. Neng Sury Sulaiman
Date Deposited: 25 Aug 2014 07:58
Last Modified: 21 Feb 2018 05:10
URI: http://umpir.ump.edu.my/id/eprint/6342
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