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

<p>This paper firstly explores the method and monitoring process of coal SPC production quality monitoring based on IOT technology and simulates different control chart patterns using the Monte Carlo method. Then a control chart pattern recognition model based on PCA-PSO-SVM is established to downscale the collected data using the PCA method and quickly discriminate whether the control chart is abnormal or not based on the OC-SVM algorithm. By identifying abnormal control chart patterns, real-time monitoring of the coal production process is realized. Finally, comparative analysis and fault source analysis are conducted to verify the sensitivity and effectiveness of the monitoring method in this paper. The difference in methane concentration between the experimental group and the comparison group is less than 10ppm, CO concentration is less than 5ppm, and dust concentration is less than 0.05mg/m<sup>3</sup>. It shows that the method of this paper is very effective for coal production monitoring.</p>
</abstract>

This paper firstly explores the method and monitoring process of coal SPC production quality monitoring based on IOT technology and simulates different control chart patterns using the Monte Carlo method. Then a control chart pattern recognition model based on PCA-PSO-SVM is established to downscale the collected data using the PCA method and quickly discriminate whether the control chart is abnormal or not based on the OC-SVM algorithm. By identifying abnormal control chart patterns, real-time monitoring of the coal production process is realized. Finally, comparative analysis and fault source analysis are conducted to verify the sensitivity and effectiveness of the monitoring method in this paper. The difference in methane concentration between the experimental group and the comparison group is less than 10ppm, CO concentration is less than 5ppm, and dust concentration is less than 0.05mg/m3. It shows that the method of this paper is very effective for coal production monitoring.

Research on the whole process monitoring and management of coal digital production based on IOT technology

Applied Mathematics and Nonlinear Sciences

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

This paper firstly explores the method and monitoring process of coal SPC production quality monitoring based on IOT technology and simulates different control...

Research on the whole process monitoring and management of coal digital production based on IOT technology

Online veröffentlicht: 11. Okt. 2023

Seitenbereich: -

Eingereicht: 13. Nov. 2022

Akzeptiert: 21. Apr. 2023

DOI: https://doi.org/10.2478/amns.2023.2.00598

Schlüsselwörter
Monte Carlo method, PCA-PSO-SVM, SPC control chart, IoT technology, Production monitoring

© 2023 Juan Tang, published by Sciendo

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

Research on the whole process monitoring and management of coal digital production based on IOT technology

Online veröffentlicht: 11. Okt. 2023

Seitenbereich: -

Eingereicht: 13. Nov. 2022

Akzeptiert: 21. Apr. 2023

DOI: https://doi.org/10.2478/amns.2023.2.00598

SchlüsselwörterMonte Carlo method, PCA-PSO-SVM, SPC control chart, IoT technology, Production monitoring

© 2023 Juan Tang, published by Sciendo

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

Schlüsselwörter
Monte Carlo method, PCA-PSO-SVM, SPC control chart, IoT technology, Production monitoring