Kevin Rose Dias
Papers
1
Total Citations
72
H-Index
1
About
Kevin Rose Dias is a robotics researcher whose work sits at the intersection of mechanical design and intelligent sensing, with a primary focus on infrastructure inspection and fault detection. His most cited contribution, "An In-Pipe Leak Detection Robot With a Neural-Network-Based Leak Verification System" (2018, 72 citations), introduces a custom-designed robot tailored for pipes of 0.203 meters in diameter—a standard size in the oil and gas industry. This work is notable for integrating onboard pressure sensors with a neural network to not only detect but also verify leaks, significantly reducing false positives. By combining a propeller-driven locomotion system with machine learning, Dias advanced the reliability of autonomous pipeline inspection, a critical need for preventing environmental and economic losses. His research demonstrates a practical, systems-level approach to deploying AI in constrained, real-world environments. With 72 citations, this paper has become a reference point for subsequent work in robotic leak detection and condition monitoring. Dias’s contributions are particularly valuable for students and researchers interested in field robotics, embedded AI, and the application of neural networks to industrial maintenance challenges.
Research Focus
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Top Papers
- 1