摘要
矿井通风机是煤矿安全生产重要的组成部分,然而煤矿环境复杂,通风机长期工作在恶劣的环境中,极易发生故障,对煤矿的安全生产造成威胁,现有的矿井主通风机状态检测与故障预警系统对于状态的检测及故障的判断都存在缺陷,难以将采集到的通风机的数据信息进行分析处理,无法准确帮助煤矿对通风机故障预警及判断。本文将采用小波包联合阶次分析故障特征提取的方式对通风机的振动信息进行处理,提取有效特征对通风机的状态或者故障进行判断。
Mine ventilator is an important part of coal mine safety production.However,the coal mine environment is complex,and the ventilator is prone to failure in the harsh environment for a long time,which will pose a threat to the safe production of coal mines.The existing mine main ventilator state detection and fault early warning system has defects in state detection and fault judgment,and it is difficult to analyze and process the collected ventilator data information,which can’t accurately help the coal mine to warn and judge the ventilator fault.In this paper,the vibration information of ventilator will be processed by wavelet packet combined with fault feature extraction of order analysis,and the effective features will be extracted to judge the state or fault of ventilator.
作者
王国栋
Wang Guodong(Ventilation Department of Guandi Coal Mine,Shanxi Coking Coal Group Company,Shanxi,030000)
出处
《当代化工研究》
2021年第17期107-108,共2页
Modern Chemical Research
关键词
通风机
检测
故障预警
安全性
ventilator
testing
fault early warning
safety
