Volume 17, Number 3

AI-Driven IoT-Enabled UAV Inspection Framework for Predictive Maintenance and Sustainable Operations in Desalination Plants

  Authors

Tariq Ali, University of Tabuk, Saudi Arabia

  Abstract

Desalination plant operation is symbolizing the solution for water scarcity situations worldwide with efficiency and sustainability. Even so, conventional inspection maintained in such infrastructures has been intensive on workforce, time-consuming and always has posed environmental and safety risks. An AIassisted IoT-enabled UAV inspection framework is proposed in this paper that is set to transform monitoring and predictive maintenance of desalination plants. This is a systematic framework that uses advanced robotics, computer vision, and machine learning to achieve autonomous UAVs for real-time anomaly detection and infrastructure inspection, as well as monitoring the environment. The main features are detection of leaks with thermal imaging, mapping of the site 3D using LiDAR for structural assessment, and the use of IoT-enabled sensors for operational parameters (salinity, temperature, etc.). From the data collected through UAVs, it would create a digital twin of the plant for detailed simulation and predictive analytics. By analyzing historic and real-time data, machine learning algorithms can predict equipment failures and optimize maintenance scheduling. It reduces inspection time, enhances operational safety, lowers maintenance costs, and assures environmental sustainability with respect to brine and chemicals leakages. The framework here provides strong potential for integration into desalination plants with integrated AI, integrated robotics and integrated UAV technologies, thus cracking open the door on bright new and smart, safe and sustainable water production systems.

  Keywords

AI-driven, IoT, UAV, 3D mapping, IoT-enabled sensors