面向选矿设备的预测性维护系统  

Predictive Maintenance System for Mineral Processing Equipment

作　　者：胡健 刘春辉 王锦 周子凯 乔百友 HU Jian;LIU Chun-Hui;WANG Jin;ZHOU Zi-Kai;QIAO Bai-You(Guanbaoshan Mining Co.Ltd.,Ansteel Group,Anshan 114041,China;School of Computer Science and Engineering,Northeastern University,Shenyang 110169,China)

机构地区：[1]鞍钢集团关宝山矿业有限公司,鞍山114041 [2]东北大学计算机科学与工程学院,沈阳110169

出　　处：《计算机系统应用》2024年第10期75-86,共12页Computer Systems & Applications

基　　金：国家自然科学基金区域创新发展联合基金(U23A20309)。

摘　　要：保障选矿设备的精准维护和稳定运行一直是矿山相关企业所面临的重要课题,而研发设备预测性维护系统已成为降低设备维护成本、提升企业生产效率的重要手段.分析了选矿设备预测性维护系统功能需求,设计了基于微服务结构的预测性维护系统架构和总体功能结构,深入阐述了系统关键技术,提出了基于多尺度CNN融合注意力机制的设备健康状态评估模型,以及基于CNN和BiLSTM的电流趋势融合预测模型,为设备预测性维护系统的构建提供了技术支撑.在鞍钢集团关宝山矿业有限公司对完成的系统进行了应用示范,并对提出的模型进行了测试.结果表明提出的模型具有较高的准确性和健壮性,优于现有模型;完成的系统能够提供精准的设备维护计划,降低了设备维护成本,并提升了企业生产效率.Ensuring the precise maintenance and stable operation of mineral processing equipment has always been an important challenge for mining-related enterprises while developing predictive maintenance systems for equipment has become a crucial means to reduce maintenance costs and improve production efficiency.This study analyzes the functional requirements of predictive maintenance systems,designs architecture and overall functional structure for a predictive maintenance system based on a micro-service architecture,and elaborates on the key technologies of the system.Moreover,the study proposes an evaluation model for equipment health status based on a multi-scale CNN fusion attention mechanism,as well as a prediction model for current trend fusion based on CNN and BiLSTM,to support the construction of the predictive maintenance system.The completed system has been applied at Ansteel Group Guanbaoshan Mining Co.Ltd.,where the proposed model undergoes testing.The results show that the proposed model outperforms existing models with its high accuracy and robustness.The developed system can provide precise equipment maintenance plans,reduce equipment maintenance costs,and improve enterprise production efficiency.

关 键 词：预测性维护系统 选矿设备 健康状态评估 电流趋势预测 

分 类 号：TD45[矿业工程—矿山机电]