摘要
为了对煤矿主要通风机故障信息进行实时诊断,以通风机振动信号为研究对象,利用小波降噪和HHT变化的方法对煤矿主要通风机的振动信号进行处理,并利用模糊神经网络理论建立了通风机故障诊断系统。通过实际验证,证明了该故障诊断系统是可行的、可靠的,为确保煤矿主要通风机稳定运行提供了技术保障。
In order to make real-time diagnosis of the fault information of the main fan in coal mine,the vibration signal of the fan was taken as the research object,and the vibration signal of the main fan in the coal mine was processed by means of wavelet denoising and HHT variation,and the fault diagnosis system of the ventilator was established by using the theory of fuzzy neural network.Through practical verification,it was proved that the fault diagnosis system was feasible and reliable,which provided technical guarantee for the stable operation of main fan in coal mines.
作者
陈杰
CHEN Jie(Zhenchengdi Mine,Xishan Coal and Electricity Group Co.,Ltd.,Taiyuan 030053,China)
出处
《煤炭科技》
2021年第1期79-81,共3页
Coal Science & Technology Magazine
