<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>As the control center of the natural gas long-distance pipeline network, the SCADA system shoulders the important tasks of data collection and monitoring of the whole long-distance pipeline, gas transmission management, production scheduling, operation and maintenance coordination in production, and plays a very important role in the whole oil and gas pipeline. In this paper, firstly, the SCADA system for localized long-distance pipelines is explained in detail, including its basic structure and special solutions to problems. Secondly, the AdaBoost algorithm, which combines the MapReduce parallel computing framework, is introduced to collect and process data from the operation logs of the SCADA system and normalize the logs. Finally, to test the interaction between the improved AdaBoost algorithm and the SCADA system, a system test was conducted. The results show that the average latency of scheduling the logs of the SCADA system by AdaBoost algorithm with MapReduce parallel computing framework is only 39.82ms, the average processing speed of the log normalization file data under the multi-threaded mode of the system reaches 86.51GB/s, and the effective accuracy of the fault diagnosis is as high as 90.36%. This shows that the oil and gas pipeline SCADA system interacting with data visualization technology can process operation logs more quickly and can carry out real-time intelligent supervision of the working status and operation parameters of the whole auxiliary system, promoting the intelligent development of the oil and gas pipeline SCADA system.</p>
</abstract>

As the control center of the natural gas long-distance pipeline network, the SCADA system shoulders the important tasks of data collection and monitoring of the whole long-distance pipeline, gas transmission management, production scheduling, operation and maintenance coordination in production, and plays a very important role in the whole oil and gas pipeline. In this paper, firstly, the SCADA system for localized long-distance pipelines is explained in detail, including its basic structure and special solutions to problems. Secondly, the AdaBoost algorithm, which combines the MapReduce parallel computing framework, is introduced to collect and process data from the operation logs of the SCADA system and normalize the logs. Finally, to test the interaction between the improved AdaBoost algorithm and the SCADA system, a system test was conducted. The results show that the average latency of scheduling the logs of the SCADA system by AdaBoost algorithm with MapReduce parallel computing framework is only 39.82ms, the average processing speed of the log normalization file data under the multi-threaded mode of the system reaches 86.51GB/s, and the effective accuracy of the fault diagnosis is as high as 90.36%. This shows that the oil and gas pipeline SCADA system interacting with data visualization technology can process operation logs more quickly and can carry out real-time intelligent supervision of the working status and operation parameters of the whole auxiliary system, promoting the intelligent development of the oil and gas pipeline SCADA system.

Log specification and intelligent analysis method based on oil and gas pipeline SCADA system

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

As the control center of the natural gas long-distance pipeline network, the SCADA system shoulders the important tasks of data collection and monitoring of...

Log specification and intelligent analysis method based on oil and gas pipeline SCADA system

Pubblicato online: 27 nov 2023

Pagine: -

Ricevuto: 10 feb 2023

Accettato: 07 giu 2023

DOI: https://doi.org/10.2478/amns.2023.2.01261

Parole chiave
AdaBoost algorithm, SCADA system, MapReduce, Intelligent analysis, Fault diagnosis

© 2023 Lingyi Sun et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Log specification and intelligent analysis method based on oil and gas pipeline SCADA system

Pubblicato online: 27 nov 2023

Pagine: -

Ricevuto: 10 feb 2023

Accettato: 07 giu 2023

DOI: https://doi.org/10.2478/amns.2023.2.01261

Parole chiaveAdaBoost algorithm, SCADA system, MapReduce, Intelligent analysis, Fault diagnosis

© 2023 Lingyi Sun et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Parole chiave
AdaBoost algorithm, SCADA system, MapReduce, Intelligent analysis, Fault diagnosis