The Design of Intelligence Anti-Running System Based Onthe Inclined Roadway

Article Preview

Abstract:

Preventing tramcar accidents is very important in the production of the coal mine. In this paper, S7-200PLC and BP neural network are used to design a intelligent flexible anti-running system. The control system adopts normally closed and flexible bufferred barrier. Following is the simulation experiment scheme. The BP neural network is adopted to eliminate the influence of temperature and filter by photo-electric coupler, which can measure the accurate and reliable tramcar speed. If the speed is faster than set point, the system will give an alarm and barrier does not work ,which stops the harvesters continuing running. This method guarantees the safe operation of coal mine.

You might also be interested in these eBooks

Info:

Periodical:

Advanced Materials Research (Volumes 962-965)

Pages:

3054-3058

Citation:

Online since:

June 2014

Export:

Price:

Permissions CCC:

Permissions PLS:

Сopyright:

© 2014 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

[1] Zhu Feng Wang. Research and application of the anti-running system for tramcar [J]. Energy Technology and Management, Volume38, Issue1, 2013, Pages 155-156.

Google Scholar

[2] Zu Lin Li. Error compensation of measuring motor current with Hall sensor[J]. Journal of Transducer Technology, Volume22, Issue 12, 2003Pages 56-67. In Chinese.

Google Scholar

[3] Lu-yu Wang, Qi Zhu. Compressed wide spectrum sensing scheme based on BP network[J]. The Journal of China Universities of Posts and Telecommunications, Volume 19, Issue 3, 2012, Pages 7-16.

DOI: 10.1016/s1005-8885(11)60258-6

Google Scholar

[4] Hua ZHang, Yun-Jia Wang, Yong-Feng Li. SVM model for estimating the parameters of the probability-integral method of predicting mining subsidence[J]. Mining Science and Technology, Volume19 , Issue3, 2009, pages 385-388.

DOI: 10.1016/s1674-5264(09)60072-7

Google Scholar

[5] Zhenhua Xie, Yu Zhang, Cai Jin. Prediction of Coal Spontaneous Combustion in Goaf Based on the BP Neural Network[J]. Procedia Engineering, Volume 43, 2012, Pages 88-92.

DOI: 10.1016/j.proeng.2012.08.016

Google Scholar

[6] SunYe. RMB Exchange Rate Forecast Approach Based on BP Neural Network[J]. Physics Procedia, Volume 33, 2012, Pages 287-293.

DOI: 10.1016/j.phpro.2012.05.064

Google Scholar

[7] Jianqing Zhang. Study on the Gas Content of Coal Seam Based on the BP Neural Network[J]. Procedia Engineering Volume 26, 2011, Pages 1554-1562.

DOI: 10.1016/j.proeng.2011.11.2338

Google Scholar

[8] Sibo Yang, T.O. Ting. Investigation of Neural Networks for Function Approxim- ation[J]. Procedia Computer Science Volume 17, 2013, Pages 586-594.

DOI: 10.1016/j.procs.2013.05.076

Google Scholar