A Deep Learning-Based Ultrasonic Pattern Recognition Method for Inspecting Girth Weld Cracking of Gas Pipeline

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.