中文摘要：

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。

英文摘要：

The large-scale vertical mill is one of key large-scale mechanical equipment for slag and cinder. The condition monitoring and fault diagnosis system is an important guarantee for the safe operation of the equipment. However, vertical mill equipment system is complex and its state- related intrinsic data is large. The systems and structures of the vertical mill are complex, and the number of the intrinsic characters relating to the conditions is very large, and these characters have complex interrelationships. Especially, the vertical mill configures a large number of sensors, the establishment of support status monitoring and fault diagnosis of highly efficient and stable data management system is very important. In this paper, the functional model and E-R model of the whole system are established from the requirements analysis of the data flow, information flow and condition monitoring proeess in the data management of the vertical mill monitoring and fault diagnosis system. The large- scale vertical mill intrinsic database and expert knowledge base, the equipment and the user information base, and the historical database design are realized ; In view of the massive data inquiry efficiency low question, according to the signal gathering time and the visit frequency, the technologies to the historical data, such as the hierarchical caching mechanism, minute table partition, the index optimization, the stored procedure and the paging display,are adopted. Based on the C # language and SQL server database, a large-scale vertical mill status monitoring and fault diagnosis database management system is developed. -dinally, an example shows that the system realizes the management of large-scale vertical mill industry data, improves the utilization rate of relevant data resources and the query efficiency of data, and provides efficient and stable data support for large-scale vertical mill status monitoring and fault diagnosis.