泊里矿主通风机振动故障分析

Vibration Failure Analysis of Main Fan in Boli Mine

井下通风是煤炭安全生产的必要条件。以泊里矿主通风机为研究对象,利用小波分析技术对故障特征信号进行提取,运用BP神经网络控制算法对故障进行训练跟踪,同时设计故障监测系统。利用LabWIEWHE Matlb平台对故障监测系统的软硬件进行分析,达到对风机振动故障参数的监测。研究表明:经过180次后基本可以达到预期效果。故障检测系统可有效提高故障识别的有效性,保障煤矿安全生产。

Underground ventilation is a necessary condition for coal safety production. Taking the main fan of Boli Mine as the research object,the wavelet analysis technology is used to extract the fault characteristic signal, the BP neural network control algorithm is used to train and track the fault, and the fault monitoring system is designed. The software and hardware of the fault monitoring system are analyzed by using LabWIEWHE Matlb platform to achieve the monitoring of fan vibration fault parameters. The research shows that the expected effect can be achieved after 180 times. The fault detection system can effectively improve the effectiveness of fault identification and ensure the safe production of coal mine.