Digital twin rapid construction method of a mining hoisting system
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摘要:
矿井提升系统的安全可靠运行对整个矿井的生产至关重要。为实现矿井提升系统的单点全域可视化与虚拟远程协同联动控制，解决传统多点视频监控只能覆盖个别关键部件，信息获取不全面的难题，结合工业传感、人工智能、快速建模、云存储等技术构建了集监测、控制、服务等功能于一身的矿井提升系统数字孪生框架，并以此为基础，提出了多维多尺度数字孪生快速建模方法。通过三维激光扫描技术与滤波、泊松三维重建等点云处理算法构建矿井提升系统的大尺度几何模型；使用工业传感网络、PLC数据读取转换技术，建立海量数据下的矿井提升系统行为模型；结合数据库技术、领域专家知识与案例，构建矿井提升系统故障知识模型。以某矿提升系统为应用场景，进行了多维多尺度数字孪生快速建模试验，获得了如下效果：几何建模效率相比传统CAD软件建模提高93%，极大程度上还原了真实场景；行为建模方法在不新增传感器和不停机的情况下实现了对真实体行为的映射，节约了大量成本，实时性强；以Unity3D软件为基础编写脚本，使几何模型、行为模型与知识模型深度融合，以几何模型的高真实度部件级模型为基础进行行为模型的同步与演绎，可以实现虚实系统间无延时的协同联动，同时通过实时行为数据可以触发知识模型进行辅助决策。整个数字孪生模型的建立过程兼顾成本与效果，将大幅度提高矿井提升系统的运行安全性与智能化程度。
Abstract:The safe and reliable operation of the hoisting system is very important for the production of the whole mine. Therefore, it is necessary to realize the single-point global visualization and virtual remote cooperative linkage control of the mining hoisting system, so as to solve the problem that the traditional multi-point video surveillance can only cover some key components and obtain incomplete information. To solving the problem, the digital twin framework of a mining hoisting system that with the function of monitoring, control and servicing is constructed by industrial sensing, artificial intelligence, rapid modeling, cloud storage and other technologies. Based on the framework, a multi-dimensional and multi-scale digital twin rapid modeling method is proposed. Firstly, a large scale geometric model of the mining hoisting system is built by 3D laser scanning technology, filtering, Poisson 3D reconstruction and other point cloud processing algorithms. Secondly, using industrial sensor network, PLC data reading and conversion technology, the behavior model of mining hoisting system under massive data is established. Lastly the fault knowledge model of the mining hoisting system is constructed using the database technology, domain expert knowledge and cases. The multi-dimensional and multi-scale digital twin rapid modeling experiment is carried out in a mine, and the results are as follows. The efficiency of the geometric modeling method has improved 93% compared with the traditional CAD modeling method and the modeling result has great similar to the real mining hoisting system. The behavior modeling method realizes the mapping of real entity behavior without adding new sensors and without shutting down, saves a lot of cost and has strong real-time performance. The driving Scripts are written based on Unity3D software to deeply integrate behavioral model, knowledge model and geometric model. Synchronization and deduction of behavioral model are carried out based on the component level model with high fidelity of the geometric model and it can realize the non-delay cooperative linkage between virtual and real systems. Meanwhile, relevant professional knowledge in the field is triggered by real-time behavioral data to assist decision making. The establishment process of the whole digital twin model takes into account the cost and effect, which will greatly improve the operation security and intelligence degree of the mining hoisting system.
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图 6 双边滤波前后点云高斯曲率对比
Figure 6. Gaussian curvature contrast diagram of point cloud before and after bilateral filtering
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图 10 矿井提升系统运行状态行为建模
Figure 10. Modeling of running state behavior of mining hoisting system
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图 11 矿井提升系统电机电流行为建模
Figure 11. Modeling of motor current behavior in mining hoisting system
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图 12 矿井提升系统电机温度行为建模
Figure 12. Modeling of motor temperature behavior in mining hoisting system
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