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Topic

Facilitating Detection and Sizing of Crack Defects in Pipes by 3D K-Means Clustering

Department of Electrical and Computer Engineering

Date & location

• Tuesday, December 17, 2024

• 10:30 A.M.

• Virtual Defence

Reviewers

Supervisory Committee

• Dr. Daler Rakhmatov, Department of Electrical and Computer Engineering, University of Victoria (Supervisor)

• Dr. Alexandra Branzan Albu, Department of Electrical and Computer Engineering, UVic (Member) 

External Examiner

• Dr. Daniela Constantinescu, Department of Mechanical Engineering, University of Victoria 

Chair of Oral Examination

• Dr. Sean Chester, Department of Computer Science, UVic 

Abstract

This thesis presents a novel approach for detection and sizing of surface-breaking crack defects in pipes using 3D K-Means clustering of ultrasound imaging data. The proposed method processes volumetric ultrasound data (obtained from a moving transducer array inside a pipe) to identify distinct clusters, effectively reducing noise and isolating critical crack-related features. Experimental validation has been performed on three pipe samples with different crack sizes and locations. The results show that 3D K-Means clustering improves crack detection and sizing, outperforming 2D K-means clustering in most cases. This research contributes to the field of ultrasonic nondestructive testing by providing an efficient solution for assessing the structural integrity of critical infrastructure components, such as pipelines.