| Citation: | PI Guoqiang, SHEN Guiyang, CHANG Haijun, et al. Research on data-driven collaborative control method for mining and transportation in fully mechanized mining face[J]. Journal of Mine Automation，2023，49（12）：47-55. doi: 10.13272/j.issn.1671-251x.2023040054
|
Currently, research on the collaborative control of shearers and scraper conveyors has preliminarily established a collaborative control mechanism for mining and transportation systems. But none of them have taken into account the uncertainty and coupling features of factors that affect the stable operation of mining and transportation systems in unstructured fully mechanized mining face environments. And the coal flow state and scraper conveyor load current are affected by the underground electrical system and cannot truly reflect the changes in scraper conveyor load. In order to solve the above problems, a collaborative control method for mining and transportation in fully mechanized mining face based on scraper conveyor load current intensification and random self-attention capsule network (RSACNN) is proposed. Based on the electrical coupling features of the electric motor current of the scraper conveyor, a current intensification model is used to preprocess the original scraper conveyor current and obtain the current component that can reflect the real load of the coal flow system. There is a highly nonlinear and uncertain relationship between the operating state parameters of the mining and transportation system in the fully mechanized mining face and the traction speed of the shearer. It is difficult to establish an accurate mathematical model. In order to solve the above problem, based on capsule neural network (CNN), the features of fine-grained features such as sudden changes in the operating state of the mining and transportation system in the fully mechanized mining face can be preserved. A collaborative control model for mining and transportation in the fully mechanized mining face based on RSACNN is established. The verification results show that compared with the self-attention capsule neural network (SACNN) method and the CNN method, the proposed RSACNN method has higher precision in predicting the traction speed of the shearer. The fitting values between the predicted speed and the actual speed have increased by 0.032 05 and 0.075 04 respectively. The average absolute error decreases by 17.7% and 22.6% respectively. The average absolute percentage error decreases by 49.9% and 71.5% respectively. The root mean square error decreases by 13.3% and 34.6% respectively.
| [1] |
王国法,王虹,任怀伟,等. 智慧煤矿2025情景目标和发展路径[J]. 煤炭学报,2018,43(2):295-305.
WANG Guofa,WANG Hong,REN Huaiwei,et al. 2025 scenarios and development path of intelligent coal mine[J]. Journal of China Coal Society,2018,43(2):295-305.
|
| [2] |
王国法,张德生. 煤炭智能化综采技术创新实践与发展展望[J]. 中国矿业大学学报,2018,47(3):459-467.
WANG Guofa,ZHANG Desheng. Innovation practice and development prospect of intelligent fully mechanized technology for coal mining[J]. Journal of China University of Mining & Technology,2018,47(3):459-467.
|
| [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] |
高有进,杨艺,常亚军,等. 综采工作面智能化关键技术现状与展望[J]. 煤炭科学技术,2021,49(8):1-22.
GAO Youjin,YANG Yi,CHANG Yajun,et al. Status and prospect of key technologies of intelligentization of fully-mechanized coal mining face[J]. Coal Science and Technology,2021,49(8):1-22.
|
| [5] |
王国法,徐亚军,张金虎,等. 煤矿智能化开采新进展[J]. 煤炭科学技术,2021,49(1):1-10.
WANG Guofa,XU Yajun,ZHANG Jinhu,et al. New development of intelligent mining in coal mines[J]. Coal Science and Technology,2021,49(1):1-10.
|
| [6] |
李首滨. 智能化开采研究进展与发展趋势[J]. 煤炭科学技术,2019,47(10):102-110.
LI Shoubin. Progress and development trend of intelligent mining technology[J]. Coal Science and Technology,2019,47(10):102-110.
|
| [7] |
原春斌. 基于多参数的刮板输送机调速研究[J]. 能源与节能,2019(9):103-104.
YUAN Chunbin. Research on speed regulation of scraper conveyor based on multi- parameter[J]. Energy and Energy Conservation,2019(9):103-104.
|
| [8] |
葛世荣. 煤矿智采工作面概念及系统架构研究[J]. 工矿自动化,2020,46(4):1-9.
GE Shirong. Research on concept and system architecture of smart mining workface in coal mine[J]. Industry and Mine Automation,2020,46(4):1-9.
|
| [9] |
陈迪蕾,郑征,黄涛,等. 基于采煤机和刮板输送机能耗模型的速度协同优化控制[J]. 煤炭学报,2022,47(6):2483-2498.
CHEN Dilei,ZHENG Zheng,HUANG Tao,et al. Coordinated optimal control of the speed of shearer and scraper conveyor based on their energy consumption models[J]. Journal of China Coal Society,2022,47(6):2483-2498.
|
| [10] |
湛玉婕. 改进BP神经网络的综采设备协同控制方法[J]. 煤炭技术,2022,41(10):207-209.
ZHAN Yujie. Collaborative control method of fully mechanized mining equipment based on improved BP neural network[J]. Coal Technology,2022,41(10):207-209.
|
| [11] |
樊占文,刘波. 基于改进Elman神经网络的综采装备协同控制研究[J]. 工矿自动化,2021,47(增刊2):26-28,38.
FAN Zhanwen,LIU Bo. Research on cooperative control of fully mechanized mining equipment based on improved Elman neural network[J]. Industry and Mine Automation,2021,47(S2):26-28,38.
|
| [12] |
FAN Qigao,LI Wei,WANG Yuqiao,et al. Control strategy for an intelligent shearer height adjusting system[J]. Mining Science and Technology,2010,20(6):908-912.
|
| [13] |
张文静. 基于PLC采煤机与刮板输送机联动控制技术研究[J]. 山东煤炭科技,2022,40(12):135-137.
ZHANG Wenjing. Research on linkage control technology of shearer and scraper conveyor based on PLC[J]. Shandong Coal Science and Technology,2022,40(12):135-137.
|
| [14] |
黄曾华,王峰,张守祥. 智能化采煤系统架构及关键技术研究[J]. 煤炭学报,2020,45(6):1959-1972.
HUANG Zenghua,WANG Feng,ZHANG Shouxiang. Research on the architecture and key technologies of intelligent coal mining system[J]. Journal of China Coal Society,2020,45(6):1959-1972.
|
| [15] |
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.
|
| [16] |
路正雄,郭卫,张帆,等. 基于数据驱动的综采装备协同控制系统架构及关键技术[J]. 煤炭科学技术,2020,48(7):195-205.
LU Zhengxiong,GUO Wei,ZHANG Fan,et al. Collaborative control system architecture and key technologies of fully-mechanized mining equipment based on data drive[J]. Coal Science and Technology,2020,48(7):195-205.
|
| [17] |
张根,丁小辉,杨骥,等. 基于多尺度自适应胶囊网络的高光谱遥感分类[J]. 激光与光电子学进展,2022,59(24):263-272.
ZHANG Gen,DING Xiaohui,YANG Ji,et al. Hyperspectral remote sensing classification using multi-scale adaptive capsule network[J]. Laser & Optoelectronics Progress,2022,59(24):263-272.
|
| [18] |
HINTON G E,KRIZHEVSKY A,WANG S D. Transforming auto-encoders[C]. 21th International Conference on Artifical Neural Networks,Espoo,2011:44-51.
|
| [19] |
杨巨成,韩书杰,毛磊,等. 胶囊网络模型综述[J]. 山东大学学报(工学版),2019,49(6):1-10.
YANG Jucheng,HAN Shujie,MAO Lei,et al. Review of capsule network[J]. Journal of Shandong University(Engineering Science),2019,49(6):1-10.
|
| [20] |
DHANABAL L,SHANTHARAJAH S P. A study on NSL-KDD dataset for intrusion detection system based on classification algorithms[J]. International Journal of Advanced Research in Computer and Communication Engineering,2015,4(6):446-452.
|
| [21] |
LU Zhengxiong,GUO Wei,ZHANG Chuanwei,et al. A novel intelligent decision-making method of shearer drum height regulating based on neighborhood rough reduction and selective ensemble learning[J]. IEEE Access,2020. DOI: 10.1109/ACCESS.2020.3048078.
|
| [22] |
LI Zhichao,LI Tian,YAN Xuefeng. A novel deep quality-supervised regularized autoencoder model for quality-relevant fault detection[J]. Science China Information Sciences,2022,65(5):276-278.
|
| [23] |
DU Yutao,ZHANG Ruiting,ZHANG Xiaowen,et al. Learning transferable and discriminative features for unsupervised domain adaptation[J]. Intelligent data analysis,2022,26(2):407-425. doi: 10.3233/IDA-215813
|
Figures(14) / Tables(3)
Supported by:
Beijing Renhe Information Technology Co. Ltd.
Visits：
DownLoad:
