Volume 49 Issue 9
Sep.  2023
Turn off MathJax
Article Contents
Abstract
References
Related Articles
CAO Xiangang, DUAN Yong, ZHAO Jiangbin, et al. Summary of research on health status assessment of fully mechanized mining equipment[J]. Journal of Mine Automation，2023，49（9）：23-35, 97. DOI: 10.13272/j.issn.1671-251x.18143
Citation: CAO Xiangang, DUAN Yong, ZHAO Jiangbin, et al. Summary of research on health status assessment of fully mechanized mining equipment[J]. Journal of Mine Automation，2023，49（9）：23-35, 97. DOI: 10.13272/j.issn.1671-251x.18143
shu

Summary of research on health status assessment of fully mechanized mining equipment

  • 1.

    College of Mechanical Engineering, Xi'an University of Science and Technology, Xi'an 710054, China

  • 2.

    Shaanxi Key Laboratory of Mine Electromechanical Equipment Intelligent Detection and Control, Xi'an 710054, China

More Information
  • Received Date: July 16, 2023
  • Revised Date: September 04, 2023
  • Available Online: September 17, 2023
    Graphical Abstract
    • Abstract
  • Fully mechanized mining equipment is gradually becoming larger, more complex and more intelligent. The traditional equipment management methods of regular maintenance and post maintenance are no longer able to meet the high reliability requirements of equipment operation in coal mine intelligent construction. Therefore, studying the relevant theories and technologies of fully mechanized equipment health status assessment has great practical significance for coal mine intelligent mining. This paper proposes the scope definition of fully mechanized mining equipment health status assessment and the fully mechanized mining equipment health status assessment process. This paper summarizes the research status and development trends of comprehensive mining equipment health status assessment methods from four aspects: signal acquisition, feature extraction and fusion, health status level classification, and health status assessment model establishment. The current challenges faced by fully mechanized mining equipment health status assessment related technologies are analyzed from aspects such as signal acquisition and sensor optimization layout, data processing and feature extraction, establishment of health status assessment models, and application of fully mechanized mining equipment status assessment. In response to the current research status and challenges mentioned above, the development trend of fully mechanized mining equipment health status assessment technology is discussed from the aspects of improving data collection schemes and fault mechanism research methods, building high-performance big data computing platforms, establishing deep learning assessment models, researching dynamic evaluation models for fully mechanized mining equipment health status, and developing fully mechanized mining equipment health status assessment systems. It is pointed out that in the process of coal mine intelligence, it is necessary to ensure that the theoretical research, algorithm development, and engineering application of fully mechanized mining equipment health status assessment go hand in hand.
    • FullText(HTML)
    • References (87)
  • [1]
    WANG Guofa,XU Yongxiang,REN Huaiwei. Intelligent and ecological coal mining as well as clean utilization technology in China:review and prospects[J]. International Journal of Mining Science and Technology,2019,29(2):161-169. DOI: 10.1016/j.ijmst.2018.06.005
    [2]
    王国法,刘峰,庞义辉,等. 煤矿智能化——煤炭工业高质量发展的核心技术支撑[J]. 煤炭学报,2019,44(2):349-357.

    WANG Guofa,LIU Feng,PANG Yihui,et al. Coal mine intellectualization:the core technology of high quality development[J]. Journal of China Coal Society,2019,44(2):349-357.
    [3]
    王国法,刘峰,孟祥军,等. 煤矿智能化(初级阶段)研究与实践[J]. 煤炭科学技术,2019,47(8):1-36.

    WANG Guofa,LIU Feng,MENG Xiangjun,et al. Research and practice on intelligent coal mine construction(primary stage)[J]. Coal Science and Technology,2019,47(8):1-36.
    [4]
    刘峰,曹文君,张建明,等. 我国煤炭工业科技创新进展及“十四五”发展方向[J]. 煤炭学报,2021,46(1):1-15.

    LIU Feng,CAO Wenjun,ZHANG Jianming,et al. Current technological innovation and development direction of the 14(th) Five-Year Plan period in China coal industry[J]. Journal of China Coal Society,2021,46(1):1-15.
    [5]
    王国法,张良,李首滨,等. 煤矿无人化智能开采系统理论与技术研发进展[J]. 煤炭学报,2023,48(1):34-53.

    WANG Guofa,ZHANG Liang,LI Shoubin,et al. Progresses in theory and technological development of unmanned smart mining system[J]. Journal of China Coal Society,2023,48(1):34-53.
    [6]
    王学文,谢嘉成,郝尚清,等. 智能化综采工作面实时虚拟监测方法与关键技术[J]. 煤炭学报,2020,45(6):1984-1996.

    WANG Xuewen,XIE Jiacheng,HAO Shangqing,et al. Key technologies of real-time virtual monitoring method for an intelligent fully mechanized coal-mining face[J] Journal of China Coal Society,2020,45(6):1984-1996.
    [7]
    王家臣. 我国综放开采40年及展望[J]. 煤炭学报,2023,48(1):83-99.

    WANG Jiachen. 40 years development and prospect of longwall top coal caving in China[J]. Journal of China Coal Society,2023,48(1):83-99.
    [8]
    FINK O,WANG Qin,SVENSEN M,et al. Potential,challenges and future directions for deep learning in prognostics and health management applications[J]. Engineering Applications of Artificial Intelligence,2020,92:103678. DOI: 10.1016/j.engappai.2020.1036783.
    [9]
    SHIN I,LEE J,LEE Y,et al. A framework for prognostics and health management applications toward smart manufacturing systems[J]. International Journal of Precision Engineering and Manufacturing-Green Technology,2018,5(4):535-554. DOI: 10.1007/s40684-018-0055-0
    [10]
    DUAN Chaoqun,DENG Chao. Prognostics of health measures for machines with aging and dynamic cumulative damage[J]. IEEE/ASME Transactions on Mechatronics,2020,25(5):2264-2275. DOI: 10.1109/TMECH.2020.2995757
    [11]
    FINK O,WANG Qin,SVENSEN M,et al. Potential,challenges and future directions for deep learning in prognostics and health management applications[J]. Engineering Applications of Artificial Intelligence,2020,92. DOI: 10.1016/j.engappai.2020.103678.
    [12]
    SINGH V,GANGSAR P,PORWAL R,et al. Artificial intelligence application in fault diagnostics of rotating industrial machines:a state-of-the-art review[J]. Journal of Intelligent Manufacturing,2023,34:931-960. DOI: 10.1007/s10845-021-01861-5
    [13]
    ZHAO Zhibin,WU Jingyao,LI Tianfu,et al. Challenges and opportunities of AI-enabled monitoring,diagnosis & prognosis:a review[J]. Chinese Journal of Mechanical Engineering,2021,34(3):16-44.
    [14]
    樊红卫,张旭辉,曹现刚,等. 智慧矿山背景下我国煤矿机械故障诊断研究现状与展望[J]. 振动与冲击,2020,39(24):194-204.

    FAN Hongwei,ZHANG Xuhui,CAO Xiangang,et al. Research status and prospect of fault diagnosis of China's coal mine machines under background of intelligent mine[J]. Journal of Vibration and Shock,2020,39(24):194-204.
    [15]
    CHE Changchagn,WANG Huawei,NI Xiaomei,et al. Hybrid multimodal fusion with deep learning for rolling bearing fault diagnosis[J]. Measurement,2020,173(7). DOI: 10.1016/j.measurement.2020.108655.
    [16]
    MA Meng,SUN Chuang,CHEN Xuefeng. Deep coupling autoencoder for fault diagnosis with multimodal sensory data[J]. IEEE Transactions on Industrial Informatics,2018,14(3):1137-1145. DOI: 10.1109/TII.2018.2793246
    [17]
    李国发,王彦博,何佳龙,等. 机电装备健康状态评估研究进展及发展趋势[J]. 吉林大学学报(工学版),2022,52(2):267-279.

    LI Guofa,WANG Yanbo,HE Jialong,et al. Research progress and development trend of health assessment of electromechanical equipment[J]. Journal of Jilin University(Engineering and Technology Edition),2022,52(2):267-279.
    [18]
    赵丽琴,刘昶,曹明生,等. 复杂装备健康度评估方法研究综述[J]. 计算机测量与控制,2021,29(11):1-7,17.

    ZHAO Liqin,LIU Chang,CAO Mingsheng,et al. Review of health assessment methods for complex equipment[J]. Computure Measurement & Control,2021,29(11):1-7,17.
    [19]
    曹现刚,雷一楠,宫钰蓉,等. 基于组合赋权法的采煤机健康状态评估方法研究[J]. 煤炭科学技术,2020,48(6):135-141.

    CAO Xiangang,LEI Yinan,GONG Yurong,et al. Study on health assessment method of shearer based on combination weighting method[J]. Coal Science and Technology,2020,48(6):135-141.
    [20]
    翟文睿,李贤功,王佳奇,等. 采煤机性能退化评估方法及应用研究[J]. 工矿自动化,2020,46(12):57-63,100.

    ZHAI Wenrui,LI Xiangong,WANG Jiaqi,et al. Research on shearer performance degradation evaluation and application[J]. Industry and Mine Automation,2020,46(12):57-63,100.
    [21]
    焦晨浩. 矿用刮板运输机运行状态在线监测系统研究[D]. 太原:太原理工大学,2021.

    JAO Chenhao. Research on on-line monitoring system of operation state of mine scraper conveyor[D]. Taiyuan:Taiyuan University of Technology,2021.
    [22]
    赵巧芝. 我国刮板输送机发展现状、趋势及关键技术[J]. 煤炭工程,2020,52(8):183-187.

    ZHAO Qiaozhi. Current status,development and key technologies of scraper conveyers[J]. Coal Engineering,2020,52(8):183-187.
    [23]
    马旭东,王跃龙,田慕琴,等. 液压支架健康评估与寿命预测模型研究[J]. 煤炭科学技术,2021,49(3):141-148.

    MA Xudong,WANG Yuelong,TIAN Muqin,et al. Health assessment and life prediction model of hydraulic support[J]. Coal Science and Technology,2021,49(3):141-148.
    [24]
    王忠乐. 综采液压支架姿态监测及控制技术[J]. 工矿自动化,2022,48(增刊2):116-117,137.

    WANG Zhongle. Attitude monitoring and control technology of fully mechanized mining hydraulic support[J]. Journal of Mine Automation,2022,48(S2):116-117,137.
    [25]
    时培涛,张吉雄,张强,等. 综合赋权的TOPSIS充填采煤液压支架评价方法研究[J]. 采矿与安全工程学报,2023,40(3):543-553.

    SHI Peitao,ZHANG Jixiong,ZHANG Qiang,et al. Comprehensive weighted TOPSIS method for evaluating the structure design of backfilling hydraulic support[J]. Journal of Mining & Safety Engineering,2023,40(3):543-553.
    [26]
    陈强. 井下乳化液泵站远程在线状态监测系统的研究与设计[D]. 南昌:南昌航空大学,2017.

    CHEN Qiang. Research and design of remote online condition monitoring system for underground emulsion pump station[D]. Nanchang:Nanchang Hangkong University,2017.
    [27]
    牛锐祥,丁华,施瑞,等. 一种乳化液泵分级故障诊断方法[J]. 液压与气动,2021,45(11):47-53.

    NIU Ruixiang,DING Hua,SHI Rui,et al. A hierarchical fault diagnosis method for emulsion pump[J]. Chinese Hydraulics & Pneumatics,2021,45(11):47-53.
    [28]
    武秋俊,王建军,郭山国. 基于DL联合信息融合技术的刮板转载机故障诊断[J]. 煤矿机械,2021,42(2):152-154.

    WU Qiujun,WANG Jianjun,GUO Shanguo. Fault diagnosis of scraper transfer machine based on DL joint with information fusion technology[J]. Coal Mine Machinery,2021,42(2):152-154.
    [29]
    王海舰,黄梦蝶,高兴宇,等. 考虑截齿损耗的多传感信息融合煤岩界面感知识别[J]. 煤炭学报,2021,46(6):1995-2008.

    WANG Haijian,HUANG Mengdie,GAO Xingyu,et al. Coal-rock interface recognition based on multi-sensor information fusion considering pick wear[J]. Journal of China Coal Society,2021,46(6):1995-2008.
    [30]
    李曼,郑思雨,刘浩东,等. 采煤机滚筒高度测量传感器工作环境磁场仿真与屏蔽研究[J]. 煤炭科学技术,2022,50(8):204-209.

    LI Man,ZHENG Siyu,LIU Haodong,et al. Study on magnetic field simulation and shielding design of shearer drum height measurement sensor working environment[J]. Coal Science and Technology,2022,50(8):204-209.
    [31]
    张强,王海舰,郭桐,等. 基于截齿截割红外热像的采煤机煤岩界面识别研究[J]. 煤炭科学技术,2017,45(5):22-27.

    ZHANG Qiang,WANG Haijian,GUO Tong,et al. Study on coal-rock interface recognition of coal shearer based on cutting infrared thermal image of picks[J]. Coal Science and Technology,2017,45(5):22-27.
    [32]
    师建国,高广财,滕睿. 激励频率自适应的采煤机状态压电俘能监测装置研究[J]. 传感技术学报,2018,31(5):683-687.

    SHI Jianguo,GAO Guangcai,TENG Rui. Study on piezoelectric energy harvesting device for state monitoring of shearer adaptive to excitation frequency[J]. Chinese Journal of Sensors and Actuators,2018,31(5):683-687.
    [33]
    张旭辉,赖正鹏,吴中华,等. 新型双稳态压电振动俘能系统的理论建模与实验研究[J]. 振动工程学报,2019,32(1):87-96.

    ZHANG Xuhui,LAI Zhengpeng,WU Zhonghua,et al. Theoretical modeling and experimental study of a new bistable piezoelectric vibration energy harvesting system[J]. Journal of Vibration Engineering,2019,32(1):87-96.
    [34]
    王国法,赵国瑞,胡亚辉. 5G技术在煤矿智能化中的应用展望[J]. 煤炭学报,2020,45(1):16-23.

    WANG Guofa,ZHAO Guorui,HU Yahui. Application prospect of 5G technology in coal mine intelligence[J]. Journal of China Coal Society,2020,45(1):16-23.
    [35]
    原景超,郭涛,王思宁. 无线低功耗采煤机状态在线监测系统设计[J]. 煤炭工程,2018,50(8):18-20.

    YUAN Jingchao,GUO Tao,WANG Sining. Design of wireless low power on line monitoring system for shearer[J]. Coal Engineering,2018,50(8):18-20.
    [36]
    郁杰,许艳霞,王文梅. 基于人工智能技术的煤矿机电设备状态识别研究[J]. 煤炭技术,2022,41(4):143-146.

    YU Jie,XU Yanxia,WANG Wenmei. Research on state identification of electromechanical equipment in coal mine based on artificial intelligence technology[J]. Coal Technology,2022,41(4):143-146.
    [37]
    张睿,张义民,朱丽莎. 采煤机截割部齿轮箱体振动特性实验[J]. 振动与冲击,2019,38(13):179-184,196.

    ZHANG Rui,ZHANG Yimin,ZHU Lisha. Tests for dynamic characteristics of shearer cutting gearbox[J]. Journal of Vibration and Shock,2019,38(13):179-184,196.
    [38]
    段蛟龙,许春雨,宋建成,等. 基于振动模型的采煤机摇臂齿轮局部故障频谱分析[J]. 工矿自动化,2016,42(7):34-39.

    DUAN Jiaolong,XU Chunyu,SONG Jiancheng,et al. Spectrum analysis of partial failure of shearer rocker gear based on vibration model[J]. Industry and Mine Automation,2016,42(7):34-39.
    [39]
    毛清华,张旭辉,马宏伟,等. 采煤机摇臂齿轮传动系统振源定位分析方法[J]. 振动. 测试与诊断,2016,36(3):466-470,602.

    MAO Qinghua,ZHANG Xuhui,MA Hongwei,et al. Vibration source location analysis method for ranging arm gear transmission system of shearer[J]. Journal of Vibration,Measurement & Diagnosis,2016,36(3):466-470,602.
    [40]
    冷军发,郭松涛,荆双喜,等. 基于最小熵解卷积的带式输送机传动滚筒轴承故障诊断[J]. 河南理工大学学报(自然科学版),2015,34(4):514-519.

    LENG Junfa,GUO Songtao,JING Shuangxi,et al. Rolling element bearing fault diagnosis of belt conveyor driving drum based on minimum entropy deconvolution[J]. Journal of Henan Polytechnic University(Natural Science),2015,34(4):514-519.
    [41]
    朱兆霞,张福建. 小波分析在采煤机故障诊断中的应用[J]. 煤炭技术,2015,34(12):247-248.

    ZHU Zhaoxia,ZHANG Fujian. Application of wavelet analysis in shearer fault diagnosis[J]. Coal Technology,2015,34(12):247-248.
    [42]
    李力,倪松松. 基于改进小波去噪预处理和EEMD的采煤机齿轮箱故障诊断[J]. 中南大学学报(自然科学版),2016,47(10):3394-3400.

    LI Li,NI Songsong. Shearer gearbox fault diagnosis based on improved wavelet denoising pretreatment and EEMD[J]. Journal of Central South University(Science and Technology),2016,47(10):3394-3400.
    [43]
    郝尚清,庞新宇,王雪松,等. 基于盲源分离的采煤机摇臂轴承故障诊断方法[J]. 煤炭学报,2015,40(11):2509-2513.

    HAO Shangqing,PANG Xinyu,WANG Xuesong,et al. Bearing fault diagnosis method for shearer rocker arm based on blind source separation[J]. Journal of China Coal Society,2015,40(11):2509-2513.
    [44]
    刘旭南,赵丽娟,付东波,等. 采煤机截割部传动系统故障信号小波包分解方法研究[J]. 振动与冲击,2019,38(14):169-175,253.

    LIU Xunan,ZHAO Lijuan,FU Dongbo,et al. Study on wavelet packet decomposition method for fault signal of shearer cutting unit transmission system[J]. Journal of Vibration and Shock,2019,38(14):169-175,253.
    [45]
    葛世荣,郝雪弟,田凯,等. 采煤机自主导航截割原理及关键技术[J]. 煤炭学报,2021,46(3):774-788.

    GE Shirong,HAO Xuedi,TIAN Kai,et al. Principle and key technology of autonomous navigation cutting for deep coal seam[J]. Journal of China Coal Society,2021,46(3):774-788.
    [46]
    郝志勇,周正啟,袁智,等. 基于实验测试的采煤机截割载荷的分形分布规律研究[J]. 应用力学学报,2019,36(2):417-423,512.

    HAO Zhiyong,ZHOU Zhengqi,YUAN Zhi,et al. Study on fractal distribution law of cutting load of shearer based on experimental tests[J]. Chinese Journal of Applied Mechanics,2019,36(2):417-423,512.
    [47]
    LI Changpeng,PENG Tianhao,ZHU Yanmin. A cutting pattern recognition method for shearers based on ICEEMDAN and improved grey wolf optimizer algorithm-optimized SVM[J]. Applied Sciences,2021,11(19). DOI: 10.3390/app11199081.
    [48]
    XU Jing,WANG Zhongbin,TAN Chao,et al. A cutting pattern recognition method for shearers based on improved ensemble empirical mode decomposition and a probabilistic neural network[J]. Sensors,2015,15(11):27721-27737. DOI: 10.3390/s151127721
    [49]
    SI Lei,WANG Zhongbin,TAN Chao,et al. A feature extraction method based on composite multi-scale permutation entropy and laplacian score for shearer cutting state recognition[J]. Measurement,2019,145:84-93. DOI: 10.1016/j.measurement.2019.05.070
    [50]
    SI Lei,WANG Zhongbin,TAN Chao,et al. A feature extraction method for shearer cutting pattern recognition based on improved local mean decomposition and multi-scale fuzzy entropy[J]. Current Science:A Fortnightly Journal of Research,2017(11). DOI: 10.18520/CS/V112/I11/2243-2252.
    [51]
    SI Lei,WANG Zhongbin,TAN Chao,et al. Vibration-based signal analysis for shearer cutting status recognition based on local mean decomposition and fuzzy c-means clustering[J]. Applied Sciences,2017,7(2). DOI: 10.3390/app7020164.
    [52]
    曹现刚,李彦川,雷卓,等. 采煤机健康状态智能评估方法研究[J]. 工矿自动化,2020,46(6):41-47.

    CAO Xiangang,LI Yanchuan,LEI Zhuo,et al. Research on intelligent evaluation method of health state of shearer[J]. Industry and Mine Automation,2020,46(6):41-47.
    [53]
    田立勇. 基于多源数据融合的采煤机截割载荷识别与预测研究[D]. 阜新:辽宁工程技术大学,2020.

    TIAN Liyong. Research on identification and prediction of shearer cutting load based on multi-source data fusion[J]. Fuxin:Liaoning Technical University,2020.
    [54]
    于宁,孙业新,陈洪月. 基于多源数据融合的采煤机截割载荷预测方法[J]. 中国机械工程,2021,32(10):1247-1253,1259.

    YU Ning,SUN Yexin,CHEN Hongyue. Prediction method of cutting loads of shearers based on multi-source data fusion[J]. China Mechanical Engineering,2021,32(10):1247-1253,1259.
    [55]
    SI Lei,WANG Zhongbin,LIU Xinhua,et al. Cutting state diagnosis for shearer through the vibration of rocker transmission part with an improved probabilistic neural network[J]. Sensors,2016,16(4). DOI: 10.3390/s16040479.
    [56]
    樊鑫,程建远,王云宏,等. 基于小波散射分解变换的煤矿微震信号智能识别[J]. 煤炭学报,2022,47(7):2722-2731. DOI: 10.13225/j.cnki.jccs.2021.1382

    FAN Xin,CHENG Jianyuan,WANG Yunhong,et al. Intelligent recognition of coal mine microseismic signal based on wavelet scattering decomposition transform[J]. Journal of China Coal Society,2022,47(7):2722-2731. DOI: 10.13225/j.cnki.jccs.2021.1382
    [57]
    DING Hua,YANG Liangliang,YANG Zhaojian. A predictive maintenance method for shearer key parts based on qualitative and quantitative analysis of monitoring data[J]. IEEE Access,2019,7:108684-108702. DOI: 10.1109/ACCESS.2019.2933676
    [58]
    曹现刚,许欣,雷卓,等. 基于降噪自编码器与改进卷积神经网络的采煤机健康状态识别[J]. 信息与控制,2022,51(1):98-106. DOI: 10.13976/j.cnki.xk.2022.1071

    CAO Xiangang,XU Xin,LEI Zhuo,et al. Health status identification of shearer based on denoising autoencoder and improved convolutional neural network[J]. Information and Control,2022,51(1):98-106. DOI: 10.13976/j.cnki.xk.2022.1071
    [59]
    SI Lei,WANG Zhongbin,LIU Xinhua,et al. A sensing identification method for shearer cutting state based on modified multi-scale fuzzy entropy and support vector machine[J]. Engineering Applications of Artificial Intelligence,2019,78:86-101. DOI: 10.1016/j.engappai.2018.11.003
    [60]
    SI Lei,WANG Zhongbin,LIU Xinhua,et al. Multi-sensor data fusion identification for shearer cutting conditions based on parallel quasi-newton neural networks and the Dempster-Shafer theory[J]. Sensors,2015,15(11):28772-28795. DOI: 10.3390/s151128772
    [61]
    彭强. 煤矿大型机械设备滚动轴承故障诊断改进方法研究[J]. 煤炭工程,2023,55(4):141-146.

    PENG Qiang. Improved methods for fault diagnosis of rolling bearings for large mechanical equipment in coal mines[J]. Coal Engineering,2023,55(4):141-146.
    [62]
    WANG Zhongbin,XU Xihua,SI Lei,et al. A dynamic health assessment approach for shearer based on artificial immune algorithm[J]. Computational Intelligence and Neuroscience,2016. DOI: 10.1155/2016/9674942.
    [63]
    郑云龙. 基于BP神经网络的刮板输送机健康状态实时评估[J]. 煤矿机械,2017,38(6):148-150.

    ZHENG Yunlong. Real-time health assessment for scraper conveyor based on BP neural network[J]. Coal Mine Machinery,2017,38(6):148-150.
    [64]
    ZHAN Jun,WANG Ronglin,YI Lingzhi,et al. Health assessment methods for wind turbines based on power prediction and mahalanobis distance[J]. International Journal of Pattern Recognition and Artificial Intelligence,2019,33(2):1951001.1-1951001.17.
    [65]
    ATAMURADOV V,MEDJAHER K,CAMCI F,et al. Railway point machine prognostics based on feature fusion and health state assessment[J]. IEEE Transactions on Instrumentation and Measurement,2018,68(8):2691-2704.
    [66]
    宋传学,肖峰,刘思含,等. 基于无迹卡尔曼滤波的轮毂电动机驱动车辆状态观测[J]. 吉林大学学报(工学版),2016,46(2):333-339.

    SONG Chuanxue,XIAO Feng,LIU Sihan,et al. Observation of vehicle state driven by wheel motor based on unscented Kalman filter[J]. Journal of Jilin University (Engineering and Technology Edition),2016,46(2):333-339.
    [67]
    朱晓荣,刘亚维. 基于模糊综合评判的光伏阵列状态评估[J]. 太阳能学报,2020,41(11):103-111.

    ZHU Xiaorong,LIU Yawei. State estimation of photovoltaic array based on fuzzy comprehensive evaluation[J]. Acta Energiae Solaris Sinica,2020,41(11):103-111.
    [68]
    LIU Zhunga,PAN Quan,DEZERT J,et al. Combination of classifiers with optimal weight based on evidential reasoning[J]. IEEE Transactions on Fuzzy Systems,2018,26(3):1217-1230. DOI: 10.1109/TFUZZ.2017.2718483
    [69]
    YIN Xiaojing,ZHANG Bangcheng,ZHOU Zhijie,et al. A new health estimation model for CNC machine tool based on infinite irrelevance and belief rule base[J]. Microelectronics Reliability,2018,84:187-196. DOI: 10.1016/j.microrel.2018.03.031
    [70]
    YANG Yifei,ZHANG Maohui,DAI Yuewei. A fuzzy comprehensive CS-SVR model-based health status evaluation of radar[J]. Plos One,2019,14(3):1-20.
    [71]
    CAI Baoping,LIU Yonghong,LIU Zengkai,et al. Application of Bayesian networks in reliability evaluation[J]. IEEE Transactions on Industrial Informatics,2019,15(4):2146-2157. DOI: 10.1109/TII.2018.2858281
    [72]
    BEHNOUSH R,YI S. Deep learning for prognostics and health management:state of the art,challenges,and opportunities[J]. Measurement,2020,163:107929. DOI: 10.1016/j.measurement.2020.107929.
    [73]
    丁飞,王谦. 液压支架结构疲劳动态可靠性评估方法[J]. 中国安全科学学报,2015,25(6):86-90.

    DING Fei,WANG Qian. Fatigue dynamic reliability assessment method of hydraulic support structure[J]. China Safety Science Journal,2015,25(6):86-90.
    [74]
    宋宇宁,徐晓辰. 基于SolidWorks和ANSYS的液压支架顶梁疲劳可靠性分析[J]. 煤炭工程,2019,51(1):91-95.

    SONG Yuning,XU Xiaochen. Analysis of the beam fatigue reliability of hydraulic support based on ANSYS and SolidWorks[J]. Coal Engineering,2019,51(1):91-95.
    [75]
    LIN Lingyan,LIN Chen,GENG Pulong,et al. Aging life evaluation of coal mining flexible EPR cables under multi-stresses[J]. IEEE Access,2020,8:53539-53546. DOI: 10.1109/ACCESS.2020.2981359
    [76]
    XING Zhizhong,GUO Wei. Analysis and research on working performance of shearer based on discrete element method[J]. IEEE Access,2019,7:121321-121331. DOI: 10.1109/ACCESS.2019.2937569
    [77]
    赖明荣. 基于状态检测的液压支架寿命预测方法研究[D]. 太原:太原理工大学,2013.

    LAI Mingrong. Prediction method of hydraulic support life based on state inspection[D]. Taiyuan:Taiyuan University of Technology,2013.
    [78]
    于健浩,祝凌甫,徐刚. 煤矿智能综采工作面安全高效开采适应性评价[J]. 煤炭科学技术,2019,47(3):60-65.

    YU Jianhao,ZHU Lingfu,XU Gang. Safety and high efficiency adaptability evaluation of coal mine intelligent fully-mechanized mining face[J]. Coal Science and Technology,2019,47(3):60-65.
    [79]
    闫向彤,董鹏辉,熊友錕,等. 基于PCA的采煤机健康状态云模型评估分析[J]. 煤炭工程,2023,55(6):152-157.

    YAN Xiangtong,DONG Penghui,XIONG Youkun,et al. Cloud model evaluation for health state of coal shearer based on PCA[J]. Coal Engineering,2023,55(6):152-157.
    [80]
    SI Lei,WANG Zhongbin,LIU Ze,et al. Health condition evaluation for a shearer through the integration of a fuzzy neural network and improved particle swarm optimization algorithm[J]. Applied Sciences,2016,6(6):171. DOI: 10.3390/app6060171
    [81]
    赵丽娟,王雅东,王斌. 含夹矸煤层条件下采煤机螺旋滚筒工作性能分析与预测[J]. 中国机械工程,2021,32(8):976-986. DOI: 10.3969/j.issn.1004-132X.2021.08.012

    ZHAO Lijuan,WANG Yadong,WANG Bin. Analysis and prediction of working performance of shearer spiral drums under coal seam with gangue[J]. China Mechanical Engineering,2021,32(8):976-986. DOI: 10.3969/j.issn.1004-132X.2021.08.012
    [82]
    刘晓波,孔屹刚,李涛,等. 采煤机调高泵隐半马尔可夫模型磨损故障预测[J]. 科学技术与工程,2020,20(29):11980-11986. DOI: 10.3969/j.issn.1671-1815.2020.29.023

    LIU Xiaobo,KONG Yigang,LI Tao,et al. Wear fault prognostics of hidden semi-markov model of shearer pump[J]. Science Technology and Engineering,2020,20(29):11980-11986. DOI: 10.3969/j.issn.1671-1815.2020.29.023
    [83]
    SI Lei,WANG Zhongbin,LIU Xinhua. A novel identification approach for shearer running status through integration of rough sets and improved wavelet neural network[J]. Proceedings of the Institution of Mechanical Engineers,Part C:Journal of Mechanical Engineering Science,2016,230(16):2792-2805.
    [84]
    曹现刚,陈瑞昊,伍宇泽,等. 基于CNN−XGBoost的采煤机健康状态评估[J]. 煤炭技术,2022,41(11):173-176. DOI: 10.13301/j.cnki.ct.2022.11.040

    CAO Xiangang,CHEN Ruihao,WU Yuze,et al. Health state assessment of coal shearer based on CNN-XGBoost[J]. Coal Technology,2022,41(11):173-176. DOI: 10.13301/j.cnki.ct.2022.11.040
    [85]
    陈相丞. 采煤机摇臂系统维修状态评估方法研究[D]. 徐州:中国矿业大学,2018.

    CHEN Xiangcheng. Study on maintenance state evaluation methods of rocker ARM system of shearer[D]. Xuzhou:China University of Mining and Technology,2018.
    [86]
    丁华,杨亮亮,杨兆建,等. 数字孪生与深度学习融合驱动的采煤机健康状态预测[J]. 中国机械工程,2020,31(7):815-823.

    DING Hua,YANG Liangliang,YANG Zhaojian,et al. Health prediction of shearers driven by digital twin and deep learning[J]. China Mechanical Engineering,2020,31(7):815-823.
    [87]
    曹怀建,贾永森,刘毅,等. 基于深度学习与SOM神经网络融合的采煤机截齿寿命预测研究[J]. 煤矿机械,2022,43(2):69-72.

    CAO Huaijian,JIA Yongsen,LIU Yi,et al. Shearer pick life prediction based on deep learning and SOM neural network fusion[J]. Coal Mine Machinery,2022,43(2):69-72.
    • Related Articles

  • Related Articles

    [1]CAI Jiahao, WANG Qianjin, FU Xiaorong, MA Xiaoping. Study on the optimal control of supply air volume in switchover process of mine main ventilators[J]. Journal of Mine Automation, 2023, 49(1): 140-145, 161. DOI: 10.13272/j.issn.1671-251x.2022050059
    [2]CUI Chuanbo, JIANG Shuguang, WANG Kai, SHAO Hao, WU Zhenyan. Adjustment of mine air volume based on air volume dispatchable model[J]. Journal of Mine Automation, 2016, 42(2): 39-43. DOI: 10.13272/j.issn.1671-251x.2016.02.010
    [3]HUAI Li, TAN Yi-chuan, CHENG Yu-long. Automatic Control System of Coal Mine Air Compressor Based on PLC[J]. Journal of Mine Automation, 2012, 38(4): 13-16.
    [4]WANG Li-hua, WANG Xiao-song, WEI Guang-jin. Design of Automatic Monitoring and Control System of Mine Main Ventilator[J]. Journal of Mine Automation, 2010, 36(12): 95-99.
    [5]WU Hai-wei, ZHANG Yi-ming, WU Zheng-yan, JIANG Shu-guang, WANG Lan-yun, WANG Jie. Development,Analysis and Comparison of Technologies of Mine-used Automatic Air Doors[J]. Journal of Mine Automation, 2010, 36(1): 61-65.
    [6]HUANG Wei. Analysis and Discussion on Disadvantages of Automatic Power off of Monitoring and Control of Local Ventilator[J]. Journal of Mine Automation, 2009, 35(8): 75-78.
    [7]CUI Guang-liang, ZHU Hong-yun, LIU Cheng-liang. Automatic Monitoring and Control System for Mine-used Main Ventilation Based on SLC500 PLC[J]. Journal of Mine Automation, 2009, 35(5): 90-92.
    [8]TAN Chang-sen, SUN Peng, GUO Feng, GU Zhi-peng. Design of Automatic Monitoring and COntrol System of Mine Main Ventilator Based on PLC[J]. Journal of Mine Automation, 2007, 33(6): 106-108.
    [9]HUANG Wen-fang, SUN Wei, YAN Gui. Application of Fuzzy Controller in the Automatic Adjustment for Fume Exhaust Throttle[J]. Journal of Mine Automation, 2007, 33(2): 33-35.

  • Cited by

    Periodical cited type(8)

    1. 吴景红. 煤矿机械故障诊断研究现状及发展趋势. 煤炭工程. 2023(06): 187-192 .
    2. 周坪，马国庆，周公博，马天兵，李远博. 智能化带式输送机健康监测技术研究综述. 仪器仪表学报. 2023(12): 1-21 .
    3. 王艳双，王建华，任磊，史更新，彭程程，臧廷朋，肖望强. 基于VMD的带式输送机齿轮箱故障诊断. 煤矿机械. 2022(10): 177-179 .
    4. 王大维. 关于矿用带式输送机使用中的动态分析. 煤矿现代化. 2020(02): 166-168 .
    5. 袁慧娟，陈兴隆. 基于SOM网络的带式输送机齿轮箱混合故障诊断研究. 煤矿机械. 2020(05): 192-195 .
    6. 刘建英. 云岗矿中B1400型带式输送机绳轮支座结构性能研究. 煤矿现代化. 2020(04): 114-116 .
    7. 沈建明，石云飞. 基于故障树的带式输送机齿轮箱故障诊断. 煤矿机械. 2020(11): 151-153 .
    8. 张金红，王菲菲，武玉英. GA-BP神经网络在带式输送机故障监测系统中的应用. 煤矿机械. 2020(12): 129-131 .

    Other cited types(2)

Catalog

    Get Citation
    PDF
    XML

    Article Metrics

    Article views (1657) PDF downloads (96) Cited by(10)
    Related

    苏ICP备 05016349号-2

    Supported by: Beijing Renhe Information Technology Co., Ltd. Visits：1225171    Baidu Analytics

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return