Feature extraction and soft computing methods for aerospace structure defect classification |
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| Affiliation: | 1. Univ. Inst. Physics Applied to Sciences and Engineering, University of Alicante, PO Box, 99, 03080 Alicante, Spain;2. Dept. Civil Engineering, University of Alicante, PO Box, 99, 03080 Alicante, Spain;1. Institute of Fundamental Technological Research, Warsaw, Poland;2. Institute of Mechanized Construction and Rock Mining, Warsaw, Poland;1. School of Architecture and Civil Engineering, Xiamen University, Xiamen, China;2. Department of Civil Engineering, Catholic University of America, Washington, DC 20064, United States;3. School of Transportation, Southeast University, Nanjing 210096, China;4. School of Automation, Southeast University, Nanjing 210096, China;1. AGH University of Science and Technology, Department of Geoinformatics and Applied Computer Science, Poland;2. Strata Mechanics Research Institute of the Polish Academy of Sciences, Poland;1. S2P Ltd., Laboratory for Motor Control and Motor Behaviour, Ljubljana, Slovenia;2. University of Ljubljana, Faculty of Sport, Laboratory for Biomechanics, Ljubljana, Slovenia;3. University of Primorska, Faculty of Mathematics, Natural Sciences and Information Technologies, Koper, Slovenia;4. University of Primorska, Andrej Marusic Institute, Department of Health Studies, Koper, Slovenia;1. School of Electronics Engineering, VIT University, Vellore, Tamil Nadu 632014, India;2. School of Electrical Engineering, VIT University, Vellore, Tamil Nadu 632014, India;3. Department of Instrument Technology, Andhra University, Visakhapatnam, Andhra Pradesh 532001, India |
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| Abstract: | This study concerns the effectiveness of several techniques and methods of signals processing and data interpretation for the diagnosis of aerospace structure defects. This is done by applying different known feature extraction methods, in addition to a new CBIR-based one; and some soft computing techniques including a recent HPC parallel implementation of the U-BRAIN learning algorithm on Non Destructive Testing data. The performance of the resulting detection systems are measured in terms of Accuracy, Sensitivity, Specificity, and Precision. Their effectiveness is evaluated by the Matthews correlation, the Area Under Curve (AUC), and the F-Measure. Several experiments are performed on a standard dataset of eddy current signal samples for aircraft structures. Our experimental results evidence that the key to a successful defect classifier is the feature extraction method – namely the novel CBIR-based one outperforms all the competitors – and they illustrate the greater effectiveness of the U-BRAIN algorithm and the MLP neural network among the soft computing methods in this kind of application. |
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| Keywords: | Non-Destructive Testing (NDT) Soft computing Feature extraction Classification algorithms Content-Based Image Retrieval (CBIR) Eddy Currents (EC) |
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