Yusuf Engin Tetik
Papers
3
Total Citations
64
H-Index
3
About
Yusuf Engin Tetik is a researcher at the forefront of non-destructive testing (NDT) and robotic inspection, specializing in the integrity of steel pipelines. His work centers on two critical, interconnected challenges: the automated interpretation of magnetic flux leakage (MFL) signals and the enabling of long-range, autonomous in-line inspection (ILI). Tetik’s major contribution is the development of a novel cascaded deep learning model that not only detects but also quantifies pipeline defects from MFL data, a significant leap beyond simple detection. This work, published in 2023 and already garnering 55 citations, demonstrates a practical, machine-learning-driven method for semi-autonomous robots to assess structural health with high precision. Complementing this, his field test of a long-range wireless communication system for ILI robots—achieving a 2.4 km operational range—addresses a critical gap in the literature for real-world deployment in natural gas distribution networks. By integrating advanced sensing with robust communication and deep learning analytics, Tetik is pushing the boundaries of how pipelines are monitored, moving toward fully autonomous, data-rich inspection regimes that promise enhanced safety and efficiency for critical energy infrastructure.
Research Focus
Key Achievements
Top Papers
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