IoT-Enabled Sensors And AI For Real-Time Monitoring of Fluid Systems

This article offers a thorough investigation of a unique IoT fluid monitoring system augmented with artificial intelligence (AI) for live data collection and predictive maintenance. This system employs high-precision pressure sensors and flow meters that demonstrate accuracy of ±0.5 bar and ±1% of reading, respectively, which offers consistent monitoring over a variety of fluid dynamics. The performance investigation demonstrated an average response time of 2 seconds, while considerable changes occurred in high turbulence settings, which highlights the vital significance of sensor location and calibration. We put into practice numerous AI models, including random forest, support vector machines (SVM), and neural networks, to analyze the gathered sensor data. The Random Forest model demonstrated the greatest performance among the others, with an accuracy of 92% that offered credible forecasts of fluid system abnormalities. A demonstration of real-time monitoring showed the system’s capacity, as it observed a 4-bar pressure reduction in under 5 minutes, allowing for fast remedial procedures. These findings underline the possibilities of merging IoT and AI technology to enhance fluid management techniques via predictive maintenance and better operational efficiency. Although the findings represent substantial improvement, persistent issues such as the potential of data loss via wireless transmission and the rigorous calibration needs still remain. Future research will stress the enhancement of communication protocols, exploring new calibration methodologies, and integrating complex AI algorithms to better the performance of the system. The results of this study offer a basis for the construction of more intelligent and more robust industrial systems, providing a substantial contribution to fluid monitoring.