朱钱祥,罗鹏平,王龙鹏,邢望,王天龙
ZHU Qianxiang,LUO Pengping,WANG Longpeng,XING Wang,WANG Tianlong

• 1:中煤科工西安研究院(集团)有限公司

摘要(Abstract)：

煤矿钻机智能施工过程中自动判识当前工序的难度较大，针对该问题提出了一种包含钻机运行过程层次建模、工序执行概率推理的工序判识方法。首先，以层次分析法对钻机运行过程中不同粒度对象间耦合过程进行描述和建模，揭示了钻机各工序执行过程中设备、功能与系统间的交互特征。其次，在上述研究基础上引入贝叶斯概率推理方法，建立工序执行概率推理模型，分析了钻机运行过程中不同粒度对象属性与各工序状态间的因果关系。随后，将采集到的传感数据进行处理并作为实时证据提供给工序判识模型，推理获得各工序的当前执行概率。最后，以ZDY23000LDK钻机运行过程中液压压力值、动力头转速及移动速度作为输入信息，利用提出的工序判识方法，推理出当前执行工序编号，实验结果显示针对上扣工序、钻进工序和起拔工序的识别准确率分别达到85.3%、81.2%和87.1%，从而证明所提工序判识方法是切实可行的。上述研究工作提供了钻机运行过程的层次解耦方法及钻机不同粒度对象间交互过程的分析方法，为后续钻机智能控制方法研究及先进智能地质装备研发提供了技术支撑。
It is very difficult to automatically identify the current process during the intelligent construction of coal mine drill rigs. In response to this, an operation process identification method was proposed, which includes hierarchical modeling of drill rig operation and probability inference of process execution. Firstly, the coupling process among the components of different granularity during the operation of drill rig is described and modeled based on the hierarchical analysis method, which reveals the interactive characteristics between equipment, function and system during the execution of each process. Secondly, Bayesian probabilistic reasoning method is introduced based on the above research to establish a process execution probability inference model, and the causal relationship between the attribute of the components of different granularity and the process status during the operation of drill rig is analyzed. Then, the collected sensing data is processed and provided as real-time evidence for the process identification model, thereby obtaining the execution probability of each drilling process by inference. Finally, the number of current executed process was inferred by the process identification method proposed herein, taking the hydraulic pressure value, the rotational speed and the movement speed of the drilling head during the operation process of ZDY23000LDK drill rig as the input. The experimental results show that the identification accuracy of the make-up process, drilling process and pull-out process reaches 85.3%,81.2% and 87.1%, respectively, proving that the proposed identification method is feasible and practical. The above research provides a hierarchical decoupling method for the operation processes of drill rig and an analysis method for the interaction process between components of different granularity of drill rigs, providing technical support for the research on intelligent control methods of drill rigs and the development of advanced intelligent geological equipment.

关键词(KeyWords)： 智能钻机;工序判识;传感数据;层次分析法;贝叶斯理论;煤矿
intelligent drill rig;process identification;sensing data;analytic hierarchy process;Bayesian theory;coal mine

Abstract:

Keywords:

基金项目(Foundation): 天地科技股份有限公司科技创新创业资金专项项目(2021-TD-MS006,2022-3-TD-KJHZ004)

作者(Author): 朱钱祥,罗鹏平,王龙鹏,邢望,王天龙
ZHU Qianxiang,LUO Pengping,WANG Longpeng,XING Wang,WANG Tianlong

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