Online Monitoring of Shearer's Pick Wear Based on ANFIS Fuzzy Information Fusion

Abstract

Abstract: In order to realize the realtime and accurate online monitoring of the wear degree in the cutting processes, the vibration signals, acoustic emission signals and temperature signals of different wear degrees were tested and extracted, and the multi feature sample databases of different wear degrees to the cutting signals were established. The optimal fuzzy membership function for each characteristic signal was calculated by the minimum ambiguity optimization model, and the method of the ANFIS multidimensional fuzzy neural network was adopted to realize the fusion of multi sensor feature informations, then the fusion results of the output confidence and weight were higher. According to the results of the random experiments of the fusion system ,the identification degree of the cutting wear monitoring system based on ANFIS fuzzy information fusion is high, and the maximum error of the test results is less than 6.5%, and the results show that the system has good fusion effect and higher test accuracy.

Key words: pick, minimum ambiguity, adaptive neuro-fuzzy inference system(ANFIS), information fusion, wear extent

摘要： 为实现对截齿截割过程中磨损程度的实时精确在线监测，分别测试和提取不同磨损程度的截齿在截割过程中的振动信号、声发射信号和温度信号，建立不同磨损程度截齿截割信号的多特征样本数据库,根据最小模糊度优化模型计算求解各特征信号的最优模糊隶属度函数，采用自适应神经-模糊推理系统多维模糊神经网络方法实现多传感特征信息的决策融合，输出置信度和权重较高的截齿磨损量融合结果。通过随机测试实验对融合系统进行验证，结果表明，基于ANFIS模糊信息融合的截齿磨损监测系统辨识度较高，测试结果最大误差在6.5%以内，系统具有良好的融合效果以及较高的测试精度。

关键词: 截齿, 最小模糊度, 自适应神经模糊推理系统, 信息融合, 磨损量

CLC Number:

TP277

Zhang Qiang, Wang Haijian, Li Liying, Liu Zhiheng. Online Monitoring of Shearer's Pick Wear Based on ANFIS Fuzzy Information Fusion[J]. China Mechanical Engineering.

张强, 王海舰, 李立莹, 刘志恒. 基于自适应神经-模糊推理系统模糊信息融合的采煤机截齿磨损在线监测[J]. 中国机械工程.

References

[1]尚慧岭.采煤机滚筒截齿失效工况的影响分析及对策[J].煤炭科学技术,2012,40(8):75-77.

Shang Huiling. Analysis and Counter Measures of Pick Failure Performance Influences of Cutting Drum in Coal Shearer[J]. Coal Science and Technology, 2012,40(8):75-77.

[2]赵丽娟,田震,郭辰光. 矿用截齿失效形式及对策[J].金属热处理,2015,40(6):194-198.

Zhao Lijuan,Tian Zhen,Guo Chenguang. Failure Modes and Counter Measures of a Mining Pick[J]. Heat Treatment of Metals, 2015,40(6):194-198.

[3]Tiryaki B. In Situ Studies on Service Life and Pick Consumption Characteristics of Shearer Drums[J]. Journal of the South African Institute of Mining and Metallurgy,2004,04(2):107-121.

[4]朱华,吴兆宏,李刚,等.煤矿机械磨损失效研究[J].煤炭学报,2006,31(3):380-385.

Zhu Hua,Wu Zhaojian,Li Gang,et al.Study on Wear Faigures of Mining Machinery[J].Journal of China Coal Society, 2006,31(3):380-385.

[5]Dewangan S, Chattopadhyaya S. Characterization of Wear Mechanisms in Distorted Conical Picks after Coal Cutting[J].Rock Mechanics & Rock Engineer-ing, 2016,49(1):225-242.

[6]张建广.EBZ260W型掘进机截割人工岩壁的截齿磨损研究[J].矿山机械,2014,42(9):11-14.

Zhang Jianguang.Study on Pick Abrasion during ArtiCial Rock Cut by EBZ260W Roadheader[J]. Mining & Processing Equipment,2014,42(9):11-14.

[7]王雁翔,杨道龙,王凌翔.镐形截齿截割磨损研究[J].煤矿机械,2014,35(2):47-49.

Wang Yanxiang, Yang Daolong, Wang Lingxiang.Study on Wear Performance of Pick Cutting[J].Coal Mine Machinery, 2014,35(2):47-49.

[8]张景异,李高彩.基于图像处理的对掘进机截齿磨损率的研究[J]. 工业仪表与自动化装置,2009(1): 22-24.

Zhang Jingyi, Li Gaocai. The Test Study of Road-Header Cutting Picks Wear Rate Based on Image Process[J].Industrial Instrumentation & Automation, 2009(1):22-24.

[9]张大伟,周帆,蒋强.图像重构技术在截齿磨损率分析中的应用[J].微计算机信息,2009,25(8/3):175-176.

Zhang Dawei, Zhou Fan, Jiang Qiang. The Application of Timage Reconstruction Technology in the Analysis of Intercept Tooth Wear Rate[J].Micro-computer Information, 2009,25(8/3):175-176.

[10]李勇,李夕兵.镐形截齿硬岩截割磨损研究[J].矿业研究与开发,2012,32(6):85-88.

Li Yong,Li Xibing.Wear Analysis Onpick-shaped Cutter in Cutting Hard Rock[J].Mining Research and Development, 2012,32(6):85-88.

[11]李晓豁,何洋,焦丽,等.基于参数识别的截割头纵向随机振动响应优化[J].中国机械工程,2015,26(6):818-823.

Li Xiaohuo, He Yang, Jiao Li, et al. Vertical Random Vibration Response and Optimization of Cutting Head Based on Parameter Identification[J]. China Mechanical Engineering, 2015,26(6):818-823.

[12]张强,王海舰,李立莹,等.基于多传感特征信息融合的采煤机截齿失效诊断[J].中国机械工程,2016,27(17):2334-2340.

Zhang Qiang,Wang Haijian,Li Liying,et al. Failure Diagnosis of Shearer's Picks Based on Fusing Information from Multi Sensors[J]. China Mechanical Engineering, 2016,27(17):2334-2340.

[13]单亚锋,孙朋,徐耀松,等.基于PSO-SVM的煤岩声发射源定位预测[J].传感技术学报,2013,26(3):402-406.

Shan Yafeng, Sun Peng, Xu Yaosong, et al.Based on the PSO-SVM Location Prediction of Coal Rock Acoustic Emission Source[J]. Chinese Journal of Sensors and Actuators, 2013,26(3):402-406.

[14]刘晓明,牟龙华,张鑫.基于信息融合的隔爆开关永磁机构储能电容失效诊断[J].煤炭学报,2014, 39(10):2121-2127.

Liu Xiaoming, Mu Longhua, Zhang Xin. Failure Diagnosis for Storage-capacitor in Permanent Magnetic Actuator of Flameproof Switchgear Based on Information Fusion[J]. Journal of China Coal Society, 2014,39(10):2121-2127.

[15]张鑫,牟龙华.基于信息融合的矿山电网复合保护的研究[J].煤炭学报,2012,37(11):1947-1952.

Zhang Xin, Mu Longhua. Research on Compound Protection for Mine Power Network Based on Information Fusion[J]. Journal of China Coal Society, 2012,37(11):1947-1952.

[16]田毅,张欣,张昕,等.汽车运行状态识别方法研究(二)—基于模糊神经网络的识别方法[J].中国机械工程,2013,24(11):1521-1524.

Tian Yi, Zhang Xin, Zhang Xin, et al. Resarch on Vehicle Driving Situation Identification(PartⅡ) -based on Fuzzy-neural Network[J]. China Mechanical Engineering, 2013,24(11):1521-1524.

[17]Panella M, Gallo A S. An Input-output Clustering Approach to the Synthesis of ANFIS Networks[J]. IEEE Transactions on Fuzzy Systems, 2005,13(1):69-81.

[18]Buragohain M, Mahanta C. A Novel Approach for ANFIS Modeling Based on Full Factorial Design[J]. Applied Soft Computing Journal,2007(3):1-33.

[19]谢苗,刘治翔,毛君.基于改进型ANFIS的磁致伸缩液位传感器温度补偿[J].传感技术学报,2015,28 (1):49-55.

Xie Miao,Liu Zhixiang,Mao Jun.Research on Temperature Compensation System of Magnetostrictive Liquid Level Sensor Based on Improved ANFIS[J]. Chinese Journal of Sensors and Actuators,2015,28(1):49-55.

[20]蒋浩宇,富立,范耀祖. ANFIS在车辆导航系统信息融合中应用的仿真研究[J].系统仿真学报,2006,18(4):1051-1054.

Jiang Haoyu, Fu Li, Fan Yaozu.Simulation Study on Application of ANFIS for Information Fusion of Vehicle Navigation System[J].Journal of System Simulation,2006,18(4):1051-1054.

[21]朱安福,景占荣,陈炜军,等. 基于ANFIS和证据理论的信息融合研究[J].中北大学学报(自然科学版),2009,30(1):74-79.

Zhu Anfu, Jing Zhanrong, Chen Weijun, et al.Data Fusion Based on ANFIS and Evidence Theory [J].Journal of North University of China(Natural Science Edition),2009,30(1):74-79.