基于模糊神经网络信息融合的采煤机煤岩识别系统

中国机械工程 ›› 2016, Vol. 27 ›› Issue (02): 201-208.

基于模糊神经网络信息融合的采煤机煤岩识别系统

张强1,2,4;王海舰1;井旺1;毛君1;袁智3;胡登高3

1.辽宁工程技术大学，阜新,123000
2.大连理工大学工业装备结构分析国家重点实验室，大连,116023
3.中国煤矿机械装备有限责任公司，北京,100011
4.四川理工学院材料腐蚀与防护四川省重点实验室，成都， 643000

出版日期:2016-01-25 发布日期:2016-12-15
基金资助:
高等学校博士学科点专项科研基金资助项目 (20132121120011);工业装备结构分析国家重点实验室开放基金资助项目(GZ1402);材料腐蚀与防护四川省重点实验室开放基金资助项目 (2014CL18);辽宁省高等学校杰出青年学者成长计划资助项目(LJQ2014036);辽宁“百千万人才工程”培养经费资助项目(2014921070);中煤集团重点科技项目(13-8)

Shearer's Coal-rock Recognition System Based on Fuzzy Neural Network Information Fusion

Zhang Qiang1,2,4;Wang Haijian1;Jing Wang1;Mao Jun1;Yuan Zhi3;Hu Denggao3

1.Liaoning Technical University,Fuxin,Liaoning,123000
2.State Key Laboratory of Structural Analysis for Industrial Equipment,Dalian University of Technology,Dalian,Liaoning,116023
3.China National Coal Mining Equipment Co., Ltd., Beijing,100011
4.Material Corrosion and Protection Key Laboratory of Sichuan Province,Sichuan University of Science & Engineering,Chengdu,643000

Online:2016-01-25 Published:2016-12-15
Supported by:

摘要/Abstract

摘要：

针对采用单一信号进行煤岩界面识别实现采煤机滚筒高度调整控制时精确度和可靠性不高的问题，提出一种基于模糊神经网络的多传感器信息融合煤岩识别方法。通过实验数据采集和分析得到不同煤岩比例截面截割过程中的振动、电流以及声功率谱信号特征样本，根据最小模糊度优化模型求得各煤岩识别信号的模糊隶属度函数，采用基于自适应神经网络模糊推理系统构建的多维模糊神经网络实现多传感器信息的决策融合，得到高可信度和精确度的滚筒调高控制量值。实验室截割实验对比以及现场随机煤岩轨迹的截割实验结果表明,采煤机滚筒截割轨迹与实际随机煤岩轨迹基本吻合，实验结果验证了系统的有效性和可靠性。

关键词: 采煤机, 模糊, 神经网络, 信息融合, 煤岩识别

Abstract:

Aiming at the low accuracy and reliability problems when using single signals to recognize the coal-rock interface for controlling and adjusting the height of shearer roller, a multi-sensor information fusion coal-rock recognition method was put forward based on fuzzy neural network. The sample characteristics of vibration, current and sound power spectrum signals were obtained through the acquisition and analyses of experimental data during cutting the section with different proportions of coal-rock, and the coal-rock recognition signals' fuzzy membership function was found according to the minimum fuzzy optimization model.The controlled measurement of roller's height with highly reliability and accuracy was obtained through multi-dimensional fuzzy neural network，which was built by adaptive neuro-fuzzy inference system. Laboratory cutting experiments and the scene cutting experiments of random coal-rock trajectory were carried out,the results show that the cutting trajectory of shearer's roller is basically the same as the random trajectory of coal-rock specimen, the results confirm the effectiveness and reliability of the system.

Key words: shearer, fuzzy, neural network, information fusion, coal-rock recognition

中图分类号:

TP76

张强, 王海舰, 井旺, 毛君, 袁智, 胡登高. 基于模糊神经网络信息融合的采煤机煤岩识别系统[J]. 中国机械工程, 2016, 27(02): 201-208.

Zhang Qiang, Wang Haijian, Jing Wang, Mao Jun, Yuan Zhi, Hu Denggao. Shearer's Coal-rock Recognition System Based on Fuzzy Neural Network Information Fusion[J]. China Mechanical Engineering, 2016, 27(02): 201-208.

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