Feature Extraction and Classification for Detecting the Thermal Faults in Electrical Installations
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 neu...
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| Format: | Article |
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Elsevier Ltd
2014
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| Online Access: | http://umpir.ump.edu.my/id/eprint/6342/ |
| _version_ | 1848817749895151616 |
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| author | M. S., Jadin Kamarul Hawari, Ghazali Soib, Taib |
| author_facet | M. S., Jadin Kamarul Hawari, Ghazali Soib, Taib |
| author_sort | M. S., Jadin |
| building | UMP Institutional Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-15T01:26:43Z |
| format | Article |
| id | ump-6342 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| last_indexed | 2025-11-15T01:26:43Z |
| publishDate | 2014 |
| publisher | Elsevier Ltd |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-63422018-02-21T05:10:35Z http://umpir.ump.edu.my/id/eprint/6342/ Feature Extraction and Classification for Detecting the Thermal Faults in Electrical Installations M. S., Jadin Kamarul Hawari, Ghazali Soib, Taib TK Electrical engineering. Electronics Nuclear engineering 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. Elsevier Ltd 2014 Article PeerReviewed 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) http://dx.doi.org/10.1016/j.measurement.2014.07.010 DOI: 10.1016/j.measurement.2014.07.010 |
| spellingShingle | TK Electrical engineering. Electronics Nuclear engineering M. S., Jadin Kamarul Hawari, Ghazali Soib, Taib Feature Extraction and Classification for Detecting the Thermal Faults in Electrical Installations |
| title | Feature Extraction and Classification for Detecting the Thermal Faults in Electrical Installations |
| title_full | Feature Extraction and Classification for Detecting the Thermal Faults in Electrical Installations |
| title_fullStr | Feature Extraction and Classification for Detecting the Thermal Faults in Electrical Installations |
| title_full_unstemmed | Feature Extraction and Classification for Detecting the Thermal Faults in Electrical Installations |
| title_short | Feature Extraction and Classification for Detecting the Thermal Faults in Electrical Installations |
| title_sort | feature extraction and classification for detecting the thermal faults in electrical installations |
| topic | TK Electrical engineering. Electronics Nuclear engineering |
| url | http://umpir.ump.edu.my/id/eprint/6342/ http://umpir.ump.edu.my/id/eprint/6342/ http://umpir.ump.edu.my/id/eprint/6342/ |