摘要
为了对煤矿井下带式输送机的核心部件滚动轴承的运行状态进行精确诊断,针对故障分类方法中支持向量机存在的惩罚因子确定困难的问题,引入交叉操作和全局最优解结合的改进人工蜂群算法,构建了故障诊断模型,通过仿真分析对比了改进模型与传统模型之间的差异性,仿真结果表明,改进的诊断模型能够快速精准地识别设备故障的类型,缩短了设备故障的诊断时间,提高了井下设备故障诊断的工作效率。
In order to accurately diagnose the operation status of rolling bearings,which is the core component of underground belt conveyors in coal mines,the improved artificial bee colony algorithm combining crossover operation and global optimal solution was introduced for the problem of difficulty in determining the penalty factor of the support vector machine in the fault classification method,and the fault diagnostic model was constructed,and the differences between the improved model and the traditional model were compared through simulation analysis.The simulation results showed that the improved diagnostic model can quickly and accurately identify the types of equipment faults,shorten the diagnostic time of equipment faults,and improve the efficiency of underground equipment fault diagnosis.
出处
《山西焦煤科技》
CAS
2024年第6期4-7,共4页
Shanxi Coking Coal Science & Technology
关键词
皮带输送机
轴承故障诊断
支持向量机
人工蜂群算法
交叉操作
全局最优理念
Belt conveyor
Bearing fault diagnosis
Support vector machine
Artificial bee colony algorithm
Crossover operation
Global optimum concept
作者简介
巩世勇(1988-),男,山西定襄人,2018年毕业于河北工程大学,助理工程师,主要从事矿井瓦斯治理工作,E-mail:619594420@qq.com。
