摘要
煤矿生产中风机一旦出现故障就会产生连锁反应,整个矿井也就无法正常进行生产,从而带来巨大的经济损失甚至人员伤亡,所以准确判断风机的运行状态,及时发现故障征兆至关重要。另外采集到矿井风机振动信号因为种种原因包含大量干扰信号,给特征分量的提取及故障诊断带来很大困难。基于此,该文运用Empirical Mode Decomposition将原始信号分解,然后利用Hilbert-Huang Transform边际谱提取矿井风机振动信号特征分量,证明了希尔伯特边际谱在矿井风机故障诊断中的优越性。
A ripple effect will take place if a failure occurs in fans in coal mine production.The whole mine production will not be able to proceed normally,and will result in huge loss of property and lives.It's essential to judge the operation state accurately and find out the symptoms of problem timely.Because of lots of jamming signals contained in collected fan vibration,it will bring difficulties to extract of feature component and fault diagnosis.This article proves that HHT marginal spectrum has advantage in fault diagnosis of mine fan by the original signal EMD decomposition,and HHT marginal spectrum feature extraction from vibration signal of mine fan components.
出处
《河北联合大学学报（自然科学版）》
CAS
2016年第2期64-69,共6页
Journal of Hebei Polytechnic University:Social Science Edition
关键词
矿井风机
故障诊断
特征分量
傅里叶变换
边际谱
mine fan
fault diagnosis
feature component
Fourier transfrom
marginal spectrum
