大型立磨状态监测数据库管理系统研究与开发

发布时间：2018-03-27 22:35

本文选题：大型立磨　切入点：数据库　出处：《机械设计与研究》2017年05期

【摘要】：大型立磨是对矿渣、煤渣等研磨加工的关键大型机械设备,状态监测和故障诊断系统是设备安全运行的重要保障,但是立磨设备系统、结构复杂,其状态有关的本征数据量大且关系复杂,而且配置有数量庞大的传感器,建立支撑状态监测和故障诊断的高效稳定的数据管理系统非常重要。从立磨状态监测和故障诊断系统数据管理中数据流、信息流、状态监测流程的需求分析出发,建立了整个系统的功能模型和E-R模型;实现了大型立磨本征数据库、专家知识库、设备及用户信息库、历史数据库的设计;针对海量数据查询效率低的问题,根据信号采集时间和访问频繁程度采用了对历史数据的分级缓存机制、分表分区、索引优化、存储过程及分页显示等技术。基于C#语言、SQL server数据库开发了大型立磨状态监测及故障诊断数据库管理系统。最后,实例验证表明:该系统实现了对大型立磨产业数据的综合管理,同时提高了相关数据资源利用率及数据的查询效率;为大型立磨状态监测及故障诊断提供了高效稳定的数据支持。
[Abstract]:Large-scale vertical mill is the key mechanical equipment for grinding slag and cinder. The condition monitoring and fault diagnosis system is an important guarantee for the safe operation of the equipment, but the structure of the vertical mill equipment system is complex. The intrinsic data associated with its state is large and complex, and it has a large number of sensors, It is very important to establish an efficient and stable data management system that supports state monitoring and fault diagnosis. Based on the requirement analysis of data flow, information flow and condition monitoring flow in vertical mill condition monitoring and fault diagnosis system, The function model and E-R model of the whole system are established, and the design of large vertical grinding intrinsic database, expert knowledge base, equipment and user information database, history database is realized. According to the time of signal acquisition and the frequency of access, the hierarchical caching mechanism of historical data is adopted, which is divided into tables and partitioned, and the index is optimized. Based on C # language SQL server database, a large vertical mill condition monitoring and fault diagnosis database management system is developed. The example shows that the system realizes the comprehensive management of large vertical mill industry data, and improves the utilization ratio of related data resources and the efficiency of data query. It provides efficient and stable data support for condition monitoring and fault diagnosis of large vertical mill.
【作者单位】：郑州轻工业学院机电工程学院;中信重工机械股份有限公司;
【基金】：国家自然科学基金(51205371,51405453) 国家科技支撑计划(2015BAF32B04)资助项目河南省高校科技创新人才支持计划项目(17HASTIT028) 郑州轻工业学院博士启动基金项目(2013BSJJ033)
【分类号】：TQ172.63

【相似文献】