Article: Optimal region growing and multi-kernel SVM for fault detection in electrical equipments using infrared thermography images Journal: International Journal of Business Intelligence and Data Mining (IJBIDM) 2020 Vol.17 No.3 pp.329 - 348 Abstract: Infrared thermography (IRT) has played an essential part in observing and examining thermal defects of electrical equipment without ending, which has vital enormity for the dependability of electrical recorded. This paper dissected the electrical parts are faulted or non-faulted with the help of segmentation and classification model. The features are calculated from the input thermal images and regions of interest (ROI) is segmented by utilising optimal region growing (ORG) technique and faults are classified using multi kernel support vector machine (MKSVM). In the tests, the classification performances from different input features are assessed. For enhancing the performance of the segmentation investigation optimisation procedure that is whale optimisation (WO) is used. Before classifying, the extracted electrical components are fused by using feature level fusion (FLF) procedure to fused vector in all images. These multi kernel classification performance indices, including sensitivity, specificity and accuracy are utilised to recognise the most appropriate input feature and the best arrangement of classifiers. The performance of SVM is contrasted with a neural network. The correlation comes about demonstrating that our technique can accomplish a superior performance with accuracy at 98.21%. Inderscience Publishers - linking academia, business and industry through research

Title: Optimal region growing and multi-kernel SVM for fault detection in electrical equipments using infrared thermography images

Authors: C. Shanmugam; E. Chandira Sekaran

Addresses: Jansons Institute of Technology, Coimbatore, India ' Coimbatore Institute of Technology, Coimbatore, India

Abstract: Infrared thermography (IRT) has played an essential part in observing and examining thermal defects of electrical equipment without ending, which has vital enormity for the dependability of electrical recorded. This paper dissected the electrical parts are faulted or non-faulted with the help of segmentation and classification model. The features are calculated from the input thermal images and regions of interest (ROI) is segmented by utilising optimal region growing (ORG) technique and faults are classified using multi kernel support vector machine (MKSVM). In the tests, the classification performances from different input features are assessed. For enhancing the performance of the segmentation investigation optimisation procedure that is whale optimisation (WO) is used. Before classifying, the extracted electrical components are fused by using feature level fusion (FLF) procedure to fused vector in all images. These multi kernel classification performance indices, including sensitivity, specificity and accuracy are utilised to recognise the most appropriate input feature and the best arrangement of classifiers. The performance of SVM is contrasted with a neural network. The correlation comes about demonstrating that our technique can accomplish a superior performance with accuracy at 98.21%.

Keywords: infrared thermography; IRT; feature extraction; support vector machine; SVM; optimisation; classification and fault detection.

DOI: 10.1504/IJBIDM.2020.109298

International Journal of Business Intelligence and Data Mining, 2020 Vol.17 No.3, pp.329 - 348

Received: 21 Nov 2017
Accepted: 07 Feb 2018
Published online: 03 Sep 2020 *

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Title: Optimal region growing and multi-kernel SVM for fault detection in electrical equipments using infrared thermography images

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