基于本体的采煤机故障诊断专家系统研究

发布时间：2019-06-19 18:54

【摘要】：采煤机是井下煤矿开采的关键设备之一,其无故障安全运行是保证安全生产的关键因素。采煤机由于自身结构的复杂性,各组成单元间错综复杂,存在各种关联耦合和不确定因素,导致其故障复杂多变,难以被发现。本文针对这些问题,采用模糊推理与本体技术相结合的方法,对采煤机故障诊断技术进行研究,对采煤机的安全运行,促进综采工作面高效生产具有重要意义。首先,从采煤机组成结构出发,对采煤机各组成单元进行分析,收集采煤机故障并分类整理进行研究,将采煤机故障分为机械故障、电气故障和液压故障,获取采煤机故障源、故障原因和故障征兆之间的内在联系,构建采煤机故障本体库。然后,依据对采煤机系统和故障的分析,提出基于描述逻辑的采煤机故障本体知识建模方法,采用OWL DL语言和protege本体建模工具对采煤机进行本体建模,建立采煤机概念、属性和实例等信息,用SWRL规则建立采煤机故障诊断规则库,并对采煤机故障知识进行逻辑一致性检查。其次,针对采煤机故障复杂性和不确定性等特点,本文采用模糊推理和专家系统相结合的推理方法,提出基于三层架构的方式对模糊推理系统进行设计,系统包括模糊知识库、模糊推理机、解释机制和人机界面四大部分,并采用MySQL数据库进行采煤机本体知识存储设计,包括数据表结构设计和数据存储。最后,对采煤机故障诊断系统总体架构进行设计,并采用Java语言和Jena API对采煤机故障诊断专家系统进行开发,设计开发登录模块、知识库管理模块、故障诊断模块和帮助模块,并通过历史故障实例对系统进行验证,印证了采煤机故障诊断专家系统的可行性和有效性,对采煤机故障诊断领域具有积极的意义。
[Abstract]:Shearer is one of the key equipment in underground coal mining, and its fault-free and safe operation is the key factor to ensure safe production. Because of the complexity of the structure and the complexity of the components of the shearer, there are various related coupling and uncertain factors, which lead to the complex and changeable faults of the shearer, which is difficult to be found. In order to solve these problems, this paper studies the fault diagnosis technology of shearer by combining fuzzy reasoning with ontology technology, which is of great significance to the safe operation of shearer and the promotion of efficient production of fully mechanized mining face. First of all, based on the composition and structure of shearer, the components of shearer are analyzed, the faults of shearer are collected and sorted out, and the faults of shearer are divided into mechanical fault, electrical fault and hydraulic fault, the internal relationship between the fault source, fault cause and fault symptom of shearer is obtained, and the fault ontology database of shearer is constructed. Then, according to the analysis of shearer system and fault, the knowledge modeling method of shearer fault ontology based on description logic is put forward. OWL DL language and protege ontology modeling tool are used to model the shearer ontology, the concept, attribute and example of shearer are established, the rule base of shearer fault diagnosis is established by SWRL rule, and the logical consistency of shearer fault knowledge is checked. Secondly, according to the characteristics of fault complexity and uncertainty of shearer, this paper adopts the reasoning method of fuzzy reasoning and expert system, and puts forward the design of fuzzy reasoning system based on three-tier architecture. The system includes four parts: fuzzy knowledge base, fuzzy inference engine, interpretation mechanism and man-machine interface, and uses MySQL database to design the knowledge storage of shearer ontology. Including data table structure design and data storage. Finally, the overall architecture of the shearer fault diagnosis system is designed, and the expert system of shearer fault diagnosis is developed by using Java language and Jena API. The login module, knowledge base management module, fault diagnosis module and help module are designed and developed. The system is verified by a historical fault example, which confirms the feasibility and effectiveness of the shearer fault diagnosis expert system. It is of positive significance to the field of shearer fault diagnosis.
【学位授予单位】：山东科技大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TP182;TD421.6

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