远程挖掘机故障诊断系统设计研究

发布时间：2018-10-19 19:24

【摘要】：挖掘机是目前使用最广泛的工程机械之一。对挖掘机可靠性和安全性的标准要求越来越高,其故障诊断的重要性愈加凸显。然而,目前国内企业开发的挖掘机故障诊断系统,存在知识库简单,故障诊断范围受限;无法替代维修专家对挖掘机故障快速诊断和准确定位;可重用性低,平台无关性差等缺点。针对上述问题,本文提出建立远程挖掘机故障诊断系统,具体工作内容包括:概述企业端与远程端的协同诊断体系结构,在B/S(Browser/Server)架构诊断型专家系统原有框架的基础上,根据系统设计需求构建了远程挖掘机故障诊断系统框架。选取回转液压系统为诊断目标研究对象,对其系统组成和工作原理进行了分析。针对传统专家系统在知识获取方面性能存在的不足,本文结合故障树分析法,在总结的理论知识和专家实际诊断经验知识基础上建立故障树模型,优化了知识获取方式。融合产生式规则表示法和框架表示法,对挖掘机故障知识表示的方法进行了改进。在知识库方面,基于E-R(Entity Relationship Diagram)的关系模型对知识实体分析,采用MySQL数据库构建了挖掘机故障知识库。并且,根据挖掘机故障知识表示特点,设计了规则框架融合的正向推理策略和相应解释机制。在理论研究的基础上,本文采用Django设计框架,使用Python、JS(JavaScript)、HTML(Hyper Text Markup Language)等语言开发了远程挖掘机故障诊断原型系统。测试运行效果良好,有效统计了挖掘机故障知识,实现了替代维修专家进行故障快速诊断和准确定位,大大提高了挖掘机故障的维修效率。
[Abstract]:Excavator is one of the most widely used construction machinery. The reliability and safety standards of excavators are becoming more and more important, and the importance of fault diagnosis is becoming more and more important. However, the current excavator fault diagnosis system developed by domestic enterprises has simple knowledge base, limited fault diagnosis scope, can not replace maintenance experts to quickly diagnose and accurately locate excavator faults, and has low reusability. Platform independence and other shortcomings. Aiming at the above problems, this paper puts forward the establishment of remote excavator fault diagnosis system. The specific work includes: summarizing the cooperative diagnosis system architecture between enterprise and remote end, based on the original framework of B / S (Browser/Server) diagnostic expert system. According to the system design requirements, a remote excavator fault diagnosis system framework is constructed. The system composition and working principle of rotary hydraulic system are analyzed. Aiming at the shortcomings of the traditional expert system in knowledge acquisition, this paper combines the fault tree analysis method, establishes the fault tree model based on the summarized theory knowledge and the expert practical diagnosis experience knowledge, and optimizes the knowledge acquisition method. The knowledge representation of excavator fault is improved by combining production rule representation and frame representation. In the aspect of knowledge base, the knowledge entity is analyzed based on the relation model of E-R (Entity Relationship Diagram), and the knowledge base of excavator fault is constructed by using MySQL database. Furthermore, according to the characteristics of excavator fault knowledge representation, the forward reasoning strategy and corresponding explanation mechanism of rule frame fusion are designed. On the basis of theoretical research, the prototype system of remote excavator fault diagnosis is developed by using Django framework and Python,JS (JavaScript), HTML (Hyper Text Markup Language) language. The test results are good, the fault knowledge of excavator is counted effectively, the fault diagnosis and accurate location are realized in place of maintenance expert, and the maintenance efficiency of excavator fault is greatly improved.
【学位授予单位】：长安大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TU621

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