基于粗糙集的采煤机故障诊断方法研究

发布时间：2018-03-03 08:13

本文选题：采煤机　切入点：故障诊断　出处：《西安科技大学》2015年硕士论文　论文类型：学位论文

【摘要】：采煤机是煤矿综合机械化采煤的关键设备,因其结构与工况复杂,故障表现形式具有多态性与隐蔽性,故障类型与征兆之间具有较强的不确定性且呈现非线性的对应关系。本文将刻画不确定性和模糊性的数学工具——粗糙集理论应用于采煤机的故障诊断,旨在探索一种采煤机故障诊断的有效方法,研究的主要内容如下:论文以采煤机截割部轴承为例建立了采煤机故障诊断决策表,通过属性离散化、属性约简以及值约简三部分进行故障诊断规则的获取。针对传统离散化方法找不到合适分割断点这一问题,把寻求最优分割断点作为一个寻优指标引入人工鱼群优化算法,通过该智能仿生算法,较好地实现了连续属性离散化,形成了故障离散决策表;针对一般属性约简方法不能得到精准的属性集,把寻求最有价值的条件属性集作为一个寻优指标引入人工鱼群算法,并通过二进制处理,较好地实现了故障离散决策表的属性约简,形成了故障约简决策表;采用一般值约简算法对故障约简决策表进行属性值约简,归纳总结出故障诊断规则。最后为了方便用户操作,实现诊断规则的自动获取,对采煤机故障诊断系统进行了总体的软件方案设计,开发了界面并调试,调试结果表明基于粗糙集的采煤机故障诊断方法具有一定的可行性与有效性。基于粗糙集的故障诊断方法有利于快速、准确地进行采煤机故障诊断,这对提高采煤机的可靠性和延长其寿命具有重要意义。
[Abstract]:The shearer is the key equipment of comprehensive mechanized coal mining in coal mine. Because of its complex structure and working condition, the form of fault expression has polymorphism and concealment. There is strong uncertainty and nonlinear correspondence between fault types and symptoms. In this paper, rough set theory, a mathematical tool for describing uncertainty and fuzziness, is applied to fault diagnosis of shearer. In order to explore an effective method for fault diagnosis of shearer, the main contents of this paper are as follows: taking the bearing of cutting part of shearer as an example, a decision table for fault diagnosis of shearer is established, and the attributes are discretized. Three parts of attribute reduction and value reduction are used to obtain fault diagnosis rules. Aiming at the problem that the traditional discretization method can not find a suitable partition breakpoint, the optimal segmentation breakpoint is introduced into the artificial fish swarm optimization algorithm as an optimization index. Through the intelligent bionic algorithm, the continuous attribute discretization is realized, and the fault discrete decision table is formed, and the general attribute reduction method can not get the accurate attribute set. The condition attribute set which is the most valuable one is introduced into artificial fish swarm algorithm, and the attribute reduction of discrete fault decision table is realized by binary processing, and the fault reduction decision table is formed. The general value reduction algorithm is used to reduce the attribute value of the fault reduction decision table, and the fault diagnosis rules are summarized. Finally, in order to facilitate the operation of the users, the automatic acquisition of the diagnosis rules is realized. The overall software scheme of fault diagnosis system of shearer is designed, and the interface is developed and debugged. The test results show that the fault diagnosis method based on rough set is feasible and effective, and the fault diagnosis method based on rough set is beneficial to the fault diagnosis of shearer quickly and accurately. It is of great significance to improve the reliability and prolong the life of shearer.
【学位授予单位】：西安科技大学
【学位级别】：硕士
【学位授予年份】：2015
【分类号】：TD421.6;TP18

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