IOT-Enabled Intelligent Drones For Leak Detection With Using Simple Text Oriented Messaging Protocol (STOMP)
Farah Amer Abdulazeez, Saadaldeen Rashid Ahmed, Basim Galeb Mejbel, Marwa Khalil Ibrahim, Bourair Al-Attar, Abadal-Salam T. Hussain
Abstract
This project aims to create IoT-enabled smart drones that detect oil pipeline breaches using STOMP. Pipelines carry fluids from source to destination. One cannot overstate these systems' economic significance. Pipelines are the most cost-effective and reliable way to transport hydrocarbon fluids worldwide. Because most lines are pressurised, leaks can create hydrocarbon fluid spills, which can degrade the environment, cause fires, or explode pipes. Pipe restrictions allow the STOMP approach to detect small outflows (below 5 mm in diameter) or minor clogs with great accuracy. Due to its improved capacity to locate and quantify pipe defects, the STOMP technique might become a global standard for oil pipeline defect identification despite its limitations. STOMP uses the Internet of Things to inject a small press throb to the tube and analyse liquid response data to detect pipe leaks. Acoustic responses for outflow and blocked faults can be dramatically altered when a tube defect zone is larger than an accidental part zone. With MATLAB and ANSYS, two 2 m-long, 25.4 mm-nominal-diameter ABS pipes were tested. Coupled outflow and blocked devices were used to model leakage and blockage defects at different pipe capacities. For leakage and blockage testing, the pipe length was 4,1 and 4,2 m with device distances of 0.1 and 0.02 m. Six sensor probes were placed along the pipe to record fluid responses after a loudspeaker pumped a pressure pulse into the pipe intake. Pipe sizes from 1 mm to 420 mm were accurately identified, whether unbroken, leaking, or clogged, in experiments. STOMP revealed blockages and leaks as tiny as 1.2 mm deep and 1 mm wide.