IoT Based monitoring and control of fluid transportation using machine learning

被引：0
|
作者：
Bhaskaran, Priyanka E. [1 ]
Maheswari, C. [1 ]
Thangavel, S. [1 ]
Ponnibala, M. [2 ]
Kalavathidevi, T. [2 ]
Sivakumar, N. S. [3 ]
机构：
[1] Kongu Engn Coll, Dept Mechatron Engn, Perundurai, India
[2] Kongu Engn Coll, Dept Elect & Instrumentat Engn, Perundurai, India
[3] Tishk Int Univ TIU, Dept Mechatron Engn, Erbil, Iraq
来源：
COMPUTERS & ELECTRICAL ENGINEERING | 2021年 / 89卷
关键词：
DCS plant; LQR based PID controller; Fluid transportation system; K-means clustering; Pressure and Flow rate; IoT;
D O I：
10.1016/j.compeleceng.2020.106899
中图分类号：
TP3 [计算技术、计算机技术];
学科分类号：
0812 ;
摘要：
It is important to concentrate on monitoring and control of the pipeline transportation system before the failure resulting in fatal accidents. To enhance the supervision performances, the SCADA (Supervisory Control and Data Acquisition) platform is incorporated with IoT by utilizing the NB-IOT module holding a high-level engineering interface. In the proposed methodology, SCADA with the LQR-PID controller serves as Local Intelligence. When the local intelligence fails to react proactively during risk occurrences, immediately its performance is deactivated by the webserver through the NB (Narrow Band)-IoT module. For experimental real-time validation of the proposed work, a lab-scale DCS (Distributed Control System) based fluid transportation system is undertaken where flow and pressure prevail to be the most influencing parameters during risk occurrences in the pipelines. Also, the performance analyses are validated experimentally using unsupervised K-means clustering to identify abnormality caused by blockage and crack in the pipeline on the cloud-stored data.
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收藏
页数：16
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