Guided-Wave-Based Damage Detection in Steel Pipes

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ACMSM25, 2020, 37, pp. 689-701
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© 2020, Springer Nature Singapore Pte Ltd. Pipelines are important infrastructural components for petro-chemical transportation. Millions of kilometres of pipelines are used to transport oil, natural gas and water. Pipeline leakage is a critical issue caused by corrosion or deterioration. This may yield to catastrophic failure or even life-threatening consequences. To prevent this, reliable and effective condition assessment should be employed to the routine maintenance. Non-destructive evaluation is the predominant method in the field of defect inspection. Guided wave testing is extensively used because of its low energy attenuation and long inspection distance. In this study, application of ultrasonic guided waves for damage detection in thin-wall steel pipes is investigated to better understand the wave propagation behaviour in presence of various damage scenarios in steel pipes. Guided waves have drawn increasing attentions in the research community of structural health monitoring (SHM) owning to their capability of identifying minor damages, however, the presence of boundaries in the structure and wave reflections produce challenges in signal processing to extract damage-related information from the disturbed signals. In this research, the commercial finite element (FE) analysis software ANSYS is used to simulate the wave propagation phenomenon in steel pipes. Longitudinal wave mode is excited on the top surface of pipe by applying external deformation. The reflection with damage information is clearly captured by ‘pitch-catch’ configuration. Multiple damage scenarios are introduced in the steel pipes by reducing the pipe thickness at different locations and with different severities. The transient response analysis is conducted to extract the dynamic wave responses in the structure followed by two wavelet-based damage indices to detect, assess and localise damage. Our extensive investigations demonstrate that the proposed method has potential for detection, assessment and localization of damage in steel pipes with a limited number of sensors.
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