Research on the Cutting Path Planning of Shearer Based on Expert System

Article Preview

Abstract:

According to the characteristics of shearer working environment and cutting work, on the base of analyze the working principle of shearer working, use the expert system theory to achieve cutting path planning. Used rule-based knowledge representation, adopted PROLOG language to compile the path planning network, knowledge acquisition used the design method based on rule skeleton and rule body and generated shearer cutting path planning knowledge base, through modifying the internal predicates can be easily achieved the knowledge base modify, storage and maintenance. Experimented in the model of MG900/2210 electrical haulage shearer 1:6 prototype, the experimental results showed that the cutting path planning can effectively improve the working reliability of shearer, by combining with human-machine interaction and remote path correction to solve the problem which can’t be solved through memory cutting and drum automatic adjustment height.

You might also be interested in these eBooks

Info:

Periodical:

Advanced Materials Research (Volumes 383-390)

Pages:

5738-5743

Citation:

Online since:

November 2011

Export:

Price:

Permissions CCC:

Permissions PLS:

Сopyright:

© 2012 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

[1] Cai Zixing, Robotics, BeiJing: Tsinghua University Press, (2009).

Google Scholar

[2] FU Jiacai, LI Hao and GUO Yong, Application of neural network in coal mining machine fault diagnosis expert system, Journal of Heilongjiang Institute of Science & Technology, vol. 17, Sep. 2007, pp.344-346, doi: 1671- 0118 (2007) 05- 0344- 03.

Google Scholar

[3] Tan Chao, Studies on Key Technologies in Remote Parametric Control Platform of Electric Haulage Coal Shearer, Doctor Degree thesis, Xuzhou: China University of Mining and Technology, (2009).

Google Scholar

[4] Wang Zhongbin and Xu Zhipeng, Research on the Technology of Shearer self-adaptive Memory Cutting, Proc. IEEE Symp. International Conference on Intelligent Computation Technology and Automation, IEEE Press, Sep. 2009, pp.920-923.

DOI: 10.1109/icicta.2009.457

Google Scholar

[5] Kelly M, Hainsworth D, Reid D, Caris C, Gurgenci H. Progress towards long wall automation. Journal of Mining Science and Technology, 2004: 769-774.

DOI: 10.1201/9780203022528-147

Google Scholar

[6] Yi Jikai and Hou Yuanbin, Intelligence Control Technology, Beijing: Beijing University of Technology Press, (2001).

Google Scholar

[7] Zhu Qijian and Pang Wenjie, Knowledge Representation and Acquisition of Fault Diagnose Expert System for Coal Mining Machines, Journal of Shandong Institute of Mining and Technology, vol. 18, Sep. 1999, pp.72-75, doi: 1000 - 2308 (1999).

Google Scholar

[8] Mohammad Hassan Shenassa and Kamran Khakpour, Knowledge Base Expert System for Tuning PID Controllers Using Wireless Technology, Proc. IEEE Symp. International Conference on Computer and Communication Engineering, IEEE Press, May, 2008, pp.310-313.

DOI: 10.1109/iccce.2008.4580618

Google Scholar

[9] QU Yan, FU Tao and QIU Hui-zhong, A Fuzzy Expert System Framework Using Object-Oriented Techniques, Proc. IEEE Symp. IEEE Pacific-Asia Workshop on Computational Intelligence and Industrial Application, IEEE Press, May, 2008, pp.474-477.

DOI: 10.1109/paciia.2008.330

Google Scholar

[10] M. Babita Jain, Amit Jain and M.B. Srinivas, A Web based Expert System Shell for Fault Diagnosis and Control of Power System Equipment, Proc. IEEE Symp. International Conference on Condition Monitoring and Diagnosis, IEEE Press, April, 2008, pp.21-24.

DOI: 10.1109/cmd.2008.4580217

Google Scholar