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http://dx.doi.org/10.6109/jkiice.2021.25.4.515

Feature Analysis of Ultrasonic Signals for Diagnosis of Welding Faults in Tubular Steel Tower  

Min, Tae-Hong (Department of Energy and Mechanical Engineering, Gyeongsang National University)
Yu, Hyeon-Tak (Department of Energy and Mechanical Engineering, Gyeongsang National University)
Kim, Hyeong-Jin (Department of Energy and Mechanical Engineering, Gyeongsang National University)
Choi, Byeong-Keun (Department of Energy and Mechanical Engineering, Gyeongsang National University)
Kim, Hyun-Sik (Mattron Corp.)
Lee, Gi-Seung (Mattron Corp.)
Kang, Seog-Geun (Department of Semiconductor Engineering, Gyeongsang National University)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.25, no.4, 2021 , pp. 515-522 More about this Journal
Abstract
In this paper, we present and analyze a method of applying a machine learning to ultrasonic test signals for constant monitoring of the welding faults in a tubular steel tower. For the machine learning, feature selection based on genetic algorithm and fault signal classification using a support vector machine have been used. In the feature selection, the peak value, histogram lower bound, and normal negative log-likelihood from 30 features are selected. Those features clearly indicate the difference of signals according to the depth of faults. In addition, as a result of applying the selected features to the support vector machine, it has been possible to perfectly distinguish between the regions with and without faults. Hence, it is expected that the results of this study will be useful in the development of an early detection system for fault growth based on ultrasonic signals and in the energy transmission related industries in the future.
Keywords
Tubular steel tower; Fault diagnosis; Ultrasonic; Machine learning; Feature analysis;
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