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Lookup NU author(s): Professor Gui Yun TianORCiD
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© 2001-2012 IEEE.Electromagnetic Acoustic Transducer (EMAT) has become one of the fastest-growing solutions for pipeline weld inspection over the past decade due to its non-contact advantage. One primary problem of EMAT is that it has relatively lower energy transition efficiency compared to widely used piezoelectric transducers, coupled with the effect of lift-off and the non-uniformity issue of welding material, the Signal-to-Noise Ratio (SNR) can be significantly restricted. This brings great difficulty in interpreting the EMAT signal measured from pipeline girth welds. To overcome this challenge, this paper presents a deep learning-based ultrasonic pattern recognition method to identify the pipeline girth weld cracking automatically. The proposed method utilizes a deep Convolution Neural Network (CNN) integrated with a pre-trained Support Vector Machine (SVM) classifier to extract the high-level features from the time-frequency representation of A-scan signals measured by bulk-wave EMAT and classify these signals into defective or non-defective groups. To validate the proposed method, a set of experiments is carried out to classify A-scan signals measured from the girth welds of an ex-service type 813-X70 gas pipeline. A comparative investigation is also undertaken to demonstrate the superiority of the proposed method against the conventional ultrasonic pattern recognition methods for evaluation.
Author(s): Yan Y, Liu D, Gao B, Tian GY, Cai ZC
Publication type: Article
Publication status: Published
Journal: IEEE Sensors Journal
Print publication date: 15/07/2020
Online publication date: 23/03/2020
Acceptance date: 02/04/2016
ISSN (print): 1530-437X
ISSN (electronic): 1558-1748
Publisher: Institute of Electrical and Electronics Engineers Inc.
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