数字孪生驱动的煤矿机电设备状态监控和诊断

Condition Monitoring and Diagnosis of Coal Mine Electromechanical Equipment Driven by Digital Twin

针对煤矿机电设备状态监控与维护的现实需求,深入研究了数字孪生技术的应用潜力。首先界定了煤矿机电设备数字孪生的内涵,进而构建了一个层次化的数字孪生架构,能够全面支持设备的全生命周期管理。在状态监控与诊断方面,提出了一种新颖的数字孪生系统架构设计,明确界定了架构的目标与功能,并详细规划了其核心组件。此外,采用了一种基于卷积神经网络(CNN)的设备故障诊断方法,提升诊断的精确性和效率。为煤矿机电设备的智能化管理和维护提供了新的理论框架和技术路径,对推动相关领域的技术进步具有重要的理论和实践意义。

Deeply studied the potential applications of digital twin technology in response to the practical demands of condition monitoring and maintenance for coal mine electromechanical equipment.Firstly defined the connotation of the digital twin for coal mine electromechanical equipment and then constructed a hierarchical digital twin architecture that fully supports the lifecycle management of the equipment.In terms of condition monitoring and diagnosis,proposed a novel digital twin system architecture design,which clearly defines the goals and functions of the architecture and details its core components.Furthermore,used a fault diagnosis method based on convolutional neural networks(CNN)to enhance diagnostic accuracy and efficiency.It provides a new theoretical framework and technical pathway for the intelligent management and maintenance of coal mine electromechanical equipment,and has theoretical and practical significant for advancing technological progress in related fields.