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Cloud Based Processing of Vibrational Signal for Oil Pipeline Monitoring System | ||
| IRAQI JOURNAL OF COMPUTERS, COMMUNICATIONS, CONTROL AND SYSTEMS ENGINEERING | ||
| Article 3, Volume 21, Issue 3, 2021, Pages 24-39 PDF (704.5 K) | ||
| Document Type: Research Paper | ||
| DOI: 10.33103/uot.ijccce.21.3.3 | ||
| Authors | ||
| Sabreen J. Siwan1; Waleed Fawwaz Shareef2; Ahmed Nasser3 | ||
| 1Control and Systems Eng. Dept., University of Technology, Baghdad, Iraq. | ||
| 2Control and system Eng. Dept. University of Technology- Iraq | ||
| 3Control and Systems Engineering | ||
| Abstract | ||
| Petroleum is the economic infrastructure in Iraq because it generates a significant portion of the country's revenue and can be considered as the primary source of financial costs each year. As a consequence, it is critical to protect the sector and continue to develop it. Therefore, it is important to track and maintain pipelines regularly to detect defects on time. In pipeline monitor and control, the advent of the Internet of Things (IoT) technology and the deployment of embedded sensing systems enable successful pipeline maintenance with the simple requirement for real-time precise measurements. In this paper, a wireless network based on an IoT system and integrated with cloud service is proposed for structure monitoring of oil pipelines, to detect the risks on the structure such as tampering and/or wear and tear effects. The method is based on collecting data from a sensor node equipped with an RF module attached to the pipeline structure. These nodes collectively form a network of IoT devices connected to the cloud server. The raw data is collected, stored, and statistically analyzed to be accessible by the user anytime and anywhere through the Internet. The performance of the system is evaluated in different cases, including the distance about the node to detect events on the pipe and to discriminate the distance of event to determine the location the event it was tested by using four different states of the transmitted data. | ||
| Keywords | ||
| Oil pipeline monitoring; IoT; AWS; ESP32 cam; Lambda function | ||
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