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工程设计学报
工程设计学报  2018, Vol. 25 Issue (3): 278-287    DOI: 10.3785/j.issn.1006-754X.2018.03.005
智能设计     
截齿磨损程度的多特征信号融合识别研究
张强1,2,3, 刘志恒1, 王海舰1, 张赫哲1
1. 辽宁工程技术大学 机械工程学院, 辽宁 阜新 123000;
2. 煤炭资源安全开采与洁净利用工程研究中心, 辽宁 阜新 123000;
3. 矿物加工科学与技术国家重点实验室, 北京 100160
Research on wear degree recognition of picks based on multi feature signal fusion
ZHANG Qiang1,2,3, LIU Zhi-heng1, WANG Hai-jian1, ZHANG He-zhe1
1. College of Mechanical Engineering, Liaoning Technical University, Fuxin 123000, China;
2. Coal Resource Safety Mining and Clean Utilization Engineering Research Center, Fuxin 123000, China;
3. State Key Laboratory of Mineral Processing Science and Technology, Beijing 100160, China
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摘要：

为了解决采煤机开采过程中截齿磨损程度在线监测和状态识别的工程难题，提出一种基于多特征信号融合的截齿磨损程度识别方法。搭建截齿磨损程度监测实验台，分别测试提取不同磨损程度截齿截割过程中的振动加速度信号、声发射信号、红外热像信号和电机电流信号，建立了截齿截割的多传感信号数据样本库；针对数据样本库中两相邻磨损状态截齿特征样本存在数据交集、系统识别精度低的问题，构建最小模糊度优化模型并计算各特征信号的最优模糊隶属度函数，获取特征样本最大隶属度。构建截齿磨损程度的神经网络识别模型，运用多特征数据样本对Back-Propagation（BP）神经网络进行学习和训练。实验结果表明：BP神经网络识别模型的识别结果和试样的实际磨损程度类别相同，此识别模型能够对截齿磨损程度类型进行实时监测和准确识别。研究结果为实际工程中截齿监测和更换提供了解决方案。
关键词： 截齿;  磨损程度;  振动信号;  声发射信号;  红外热像信号;  电机电流信号    
Abstract:

In order to solve the engineering problems of on-line monitoring and state recognition for the wear degree of picks during mining process, a method to recognize the wear degree of picks based on multi feature signal fusion was proposed. An experimental platform for recognizing the wear degree of picks was set up, and the vibration acceleration signal, acoustic emission signal, infrared thermal signal and motor current signal in cutting process were extracted and tested respectively. A sample library of multi-sensor data for picks cutting was established. Aiming at the problems of existing data intersection between two adjacent samples of wear states, which reduced the system recognition accuracy, the minimum fuzzyness optimization model was established to calculate the optimal fuzzy membership function of each characteristic signal and the maximum membership degree of feature samples were obtained. A neural network identification model for different wear degree of picks was constructed. The Back-Propagation (BP) neural network was studied and trained by using multi feature data samples. The experimental results showed that the BP network discriminant results of the recognition model were consistent with the actual type, and this recognition model could accurately monitor and recognize the type of wear degree of picks. The research results provide a solution for monitoring and replacing picks in practical engineering.
Key words: pick    wear degree    vibration signal    acoustic emission signal    infrared thermal image signal    motor current signal
收稿日期: 2017-06-14 出版日期: 2018-06-28
CLC:  TP277  
基金资助:

国家自然科学基金资助项目（51504121）；辽宁省自然科学基金资助项目（201601324）；煤炭资源安全开采与洁净利用工程研究中心开放课题（LNTU16KF02）
作者简介: 张强(1980-),男,辽宁岫岩人,教授,博士生导师,博士,从事矿山机械动态设计及可靠性研究,E-mail:lgdjx042@126.com,http://orcid.org/0000-0003-1322-4794
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引用本文:

张强, 刘志恒, 王海舰, 张赫哲. 截齿磨损程度的多特征信号融合识别研究[J]. 工程设计学报, 2018, 25(3): 278-287.

ZHANG Qiang, LIU Zhi-heng, WANG Hai-jian, ZHANG He-zhe. Research on wear degree recognition of picks based on multi feature signal fusion[J]. Chinese Journal of Engineering Design, 2018, 25(3): 278-287.

链接本文:

https://www.zjujournals.com/gcsjxb/CN/10.3785/j.issn.1006-754X.2018.03.005    或    https://www.zjujournals.com/gcsjxb/CN/Y2018/V25/I3/278

[1] 付林,杜长龙,刘送永,等.螺旋钻采煤机钻头截齿载荷特性研究[J].中国机械工程,2013,24(15):2020-2024. FU Lin, DU Chang-long, LIU Song-yong, et al. Research on load characteristics of picks on auger drill miner's aiguille[J]. China Mechanical Engineering, 2013, 24(15):2020-2024.
[2] 刘浩,邱大龙,田冬林,等.基于ANSYS采煤机截齿结构改进设计及应力分析[J].煤矿机械,2013,34(10):167-169. LIU Hao, QIU Da-long, TIAN Dong-lin, et al. Structure improvement design and stress analysis of coal winning machine cutting tooth based on ANSYS[J]. Coal Mine Machinery, 2013, 34(10):167-169.
[3] 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 Zhao-hong, LI Gang, et al. Study on wear failures of mining machinery[J]. Journal of China Coal Society, 2006, 31(3):380-385.
[4] 张延强,李秀红,任家骏,等.WK-75型矿用挖掘机斗齿的磨损分析[J].工程设计学报,2015,22(5):493-498. ZHANG Yan-qiang, LI Xiu-hong, REN Jia-jun, et al. Wear analysis on bucket tooth of WK-75 mining excavator[J]. Chinese Journal of Engineering Design, 2015, 22(5):493-498.
[5] 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 Hui-ling. Analysis and countermeasures of pick failure performance influences of cutting drum in coal shearer[J]. Coal Science and Technology, 2012, 40(8):75-77.
[6] DEWANGAN S, CHATTOPADHYAYA S. Characterization of wear mechanisms in distorted conical picks after coal cutting[J]. Rock Mechanics and Rock Engineering, 2016, 49(1):225-242.
[7] 刘春生,王庆华,李德根.镐型截齿截割阻力谱的分形特征与比能耗模型[J].煤炭学报,2015,40(11):2623-2628. LIU Chun-sheng, WANG Qing-hua, LI De-gen. Fractal characteristic and specific energy model of conical picks cutting resistance spectrum[J]. Journal of China Coal Society, 2015, 40(11):2623-2628.
[8] 陆辉,王义亮,杨兆建.采煤机镐形截齿疲劳寿命分析及优化[J].煤炭科学技术,2013,41(7):100-102,106. LU Hui, WANG Yi-liang, YANG Zhao-jian. Analysis and optimization on fatigue life of pick cutter for cutting drum of coal shearer[J]. Coal Science and Technology, 2013, 41(7):100-102, 106.
[9] 李祥松.TRIZ理论在掘进机截割头设计中的应用[J].工程设计学报,2015,22(3):219-223. LI Xiang-song. Application of TRIZ theory in the design of roadheader cutting-head[J]. Chinese Journal of Engineering Design, 2015, 22(3):219-223.
[10] 姚继权,李晓豁,焦丽.基于Pro/E和ANSYS的镐型截齿有限元分析[J].矿山机械,2008,36(21):17-19. YAO Ji-quan, LI Xiao-huo, JIAO Li. Finite element analysis of pick-shaped cutter based on Pro/ENGINEER and ANSYS[J]. Mining & Processing Equipment, 2008, 36(21):17-19.
[11] 张梦奇,郝建生,马健康.锥形截齿旋转破岩截割力预测方法试验研究[J].煤炭科学技术,2015,43(12):98-103. ZHANG Meng-qi, HAO Jian-sheng, MA Jian-kang. Experimental study on rock cutting force by rotational moving for coalmining equipment conical pick[J]. Coal Science and Technology, 2015, 43(12):98-103.
[12] 张强,王海舰,李立莹,等.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,2016,27(19):2607-2613. ZHANG Qiang, WANG Hai-jian, LI Li-ying, et al. Online monitoring of shearer's pick wear based on ANFIS fuzzy information fusion[J]. China Mechanical Engineering, 2016, 27(19):2607-2613.
[13] 张强,王海舰,王兆,等.基于红外热像检测的截齿煤岩截割特性与闪温分析[J].传感技术学报,2016,29(5):686-692. ZHANG Qiang, WANG Hai-jian, WANG Zhao, et al. Analysis of coal-rock's cutting characteristics and flash temperature for peak based on infrared thermal image testing[J]. Chinese Journal of Sensors Actuators, 2016, 29(5):686-692.
[14] 张强,刘志恒,王海舰,等.基于BP神经网络的截齿磨损程度在线监测[J].中国机械工程,2017,28(9):1062-1068. ZHANG Qiang, LIU Zhi-heng, WANG Hai-jian, et al. On-line monitoring of pick's wear degrees based on BP neural network[J]. China Mechanical Engineering, 2017, 28(9):1062-1068.
[15] 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 Hai-jian, LI Li-ying, et al. Failure diagnosis of shearer's picks based on information fusion from multi sensors[J]. China Mechanical Engineering, 2016, 27(17):2334-2340.
[16] 张强,王海舰,井旺,等.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(2):201-208. ZHANG Qiang, WANG Hai-jian, JING Wang, et al. Shearer's coal-rock recognition system based on fuzzy neural network information fusion[J]. China Mechanical Engineering, 2016, 27(2):201-208.
[17] 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 Xiao-ming, MU Long-hua, 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.
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