Journal
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
Volume 69, Issue 2, Pages 314-316Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TIM.2019.2956300
Keywords
Insulators; Feature extraction; Inspection; Polymers; Gray-scale; Artificial neural networks; Testing; Artificial neural network (ANN); convolutional neural network (CNN); image processing; machine learning (ML)
Funding
- Department of Electrical Engineering, American University of Sharjah, Sharjah, United Arab Emirates
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This article proposes a new automated inspection system that can estimate erosion in silicone rubber (SIR) samples using a computer vision-based method. In this work, we used SIR samples that were damaged under laboratory conditions. The proposed work is expected to classify SIR samples into one of three classes based on the degree of erosion following the IEC-60587 standard in defining failed samples. We use various preprocessing and feature extraction methods and classify using the artificial neural network (ANN) and deep convolutional neural network (CNN). We compare their performance and find that the best results were achieved using a deep CNN architecture. This work serves as a proof of concept and can be further extended to outdoor on-field test cases.
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