IoT Sensors to Monitor Pipeline Pressure and Flow Rate Combined with ML-Algorithms to Detect Leakages

Year : 2025 | Volume : 12 | Issue : 02 | Page : 40 48
    By

    Nikat Rajak Mulla,

  • Kazi Kutubuddin Sayyad Liyakat,

  1. Student, Department of Electronics and Telecommunication Engineering, Brahmdevdada Mane Institute of Technology, Solapur, Maharashtra, India
  2. Professor and Head, Department of Electronics and Telecommunication Engineering, Brahmdevdada Mane Institute of Technology, Solapur, Maharashtra, India

Abstract

In the field of fluid mechanics, pipelines are the lifeblood of industries, transporting everything from natural gas and oil to water and chemicals. Maintaining their integrity is paramount for safety, economic efficiency, and environmental protection. Traditional leak detection methods explained in fluid mechanics can be slow, expensive, and sometimes fail to identify small leaks early enough to prevent significant damage. However, the convergence of Internet of Things (IoT) and Machine Learning (ML) is ushering in a new era of proactive and intelligent pipeline management. The core of this revolution lies in the deployment of IoT sensors along the pipeline’s length. These sensors provide a constant stream of real-time data on critical parameters. As sensor technology and data analysis methods continue to improve, their applications in pipeline systems are expected to grow even further. These advancements support not only more secure and efficient pipeline operations but also contribute to long-term sustainability goals. The adoption of these technologies marks a shift toward smarter infrastructure and more reliable resource management for the future. The raw data from IoT sensors is not immediately actionable. It needs to be processed, analyzed, and compared against historical trends and operational parameters. The integration of Machine Learning (ML) with Internet of Things (IoT) technology is bringing significant improvements to the way pipelines are monitored and maintained. By utilizing smart sensors along pipelines, it becomes possible to gather real-time data on essential parameters such as pressure, flow rate, and temperature. ML algorithms process this data to detect unusual patterns or changes that could indicate issues like leaks or structural weaknesses. This approach provides a more efficient and intelligent way to manage fluid transport systems and is becoming increasingly relevant in the field of fluid mechanics. Traditional pipeline monitoring methods are often reactive, addressing problems only after they occur. In contrast, the combination of IoT and ML enables predictive maintenance by identifying problems early, allowing operators to take action before serious damage happens. This leads to improved safety, reduced repair costs, and minimized environmental harm. ML algorithms excel at this task. The combination of IoT sensors and machine learning algorithms is revolutionizing pipeline monitoring like flow rate, pressure, and offering a powerful and proactive approach to leak detection and prevention. This is employing in the study of fluid mechanics. By embracing these technologies, pipeline operators can significantly improve safety, reduce costs, and minimize their environmental impact, paving the way for a more sustainable and secure future. Asthe technology continuesto evolve, we can expect even greater advancements in pipeline management, ensuring the safe and efficient transportation of vital resources for generations to come.

Keywords: Fluid mechanics, flow rate, pipe leakage, pipeline, pressure, IoT, machine learning

[This article belongs to Recent Trends in Fluid Mechanics ]

How to cite this article:
Nikat Rajak Mulla, Kazi Kutubuddin Sayyad Liyakat. IoT Sensors to Monitor Pipeline Pressure and Flow Rate Combined with ML-Algorithms to Detect Leakages. Recent Trends in Fluid Mechanics. 2025; 12(02):40-48.
How to cite this URL:
Nikat Rajak Mulla, Kazi Kutubuddin Sayyad Liyakat. IoT Sensors to Monitor Pipeline Pressure and Flow Rate Combined with ML-Algorithms to Detect Leakages. Recent Trends in Fluid Mechanics. 2025; 12(02):40-48. Available from: https://journals.stmjournals.com/rtfm/article=2025/view=222907


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Regular Issue Subscription Review Article
Volume 12
Issue 02
Received 31/05/2025
Accepted 02/06/2025
Published 10/07/2025
Publication Time 40 Days
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