煤矿主通风机轴承振动在线监测与故障诊断技术研究  

Online monitoring and early fault diagnosis technology for bearing vibration of main ventilator in coal mines

作　　者：徐楠 XU Nan(Electromechanical Management Department,CHN Energy Shendong Coal Group Co.,Ltd.,Shenmu 719315,China)

机构地区：[1]神华神东煤炭集团机电管理部,陕西神木719315

出　　处：《煤炭工程》2025年第2期218-224,共7页Coal Engineering

摘　　要：为发现轴承早期故障,研究了轴承振动在线监测与故障诊断技术,提出了煤矿主通风机轴承振动在线监测系统总体架构。在计算轴承故障频率的基础上,结合现场工况要求,开展了测点确定和传感器选型以及安装方式研究,设计了由数据采集板、电源转换模块、漏电保护开关、电源模块、防爆箱组成的数据采集单元;针对轴承早期故障引起的振动信号微弱且易被现场强背景噪声淹没的问题,利用现场实测风机轴承振动信号,研究了基于TQWT的轴承故障诊断方法和基于TQWT-KSVD的轴承故障诊断方法。结果表明:与TQWT相比,TQWT-KSVD方法能够从风机轴承现场振动信号提取周期性的全局特征,所提取的幅值是基于TQWT方法的两倍以上。验证了TQWT-KSVD方法用于煤矿主通风机轴承早期故障诊断的有效性。To detect early bearing faults,the online vibration monitoring and fault diagnosis technology of main ventilator bearing in coal mine was studied,and the overall system architecture was proposed.Based on calculating bearing fault frequency and combining with the requirements of field working conditions,the detection point determination,sensor selection,and installation method were carried out.A data acquisition unit composed of data acquisition board,power supply conversion module,leakage protection switch,power supply module and the explosion-proof box was designed.To solve the problem that the vibration signal caused by early bearing fault is weak and easy to be submerged by strong field background noise,the bearing fault diagnosis method based on TQWT and the bearing fault diagnosis method based on TQWT-KSVVD were studied using the field measured fan bearing vibration signal.The results show that,compared with TQWT,the TQWT-KSVD method can extract periodic global features from the field vibration signals of fan bearings,and the extracted amplitude is more than twice that of the TQWT-based method.The validity of the TQWT-KSVD method for early fault diagnosis of coal mine main ventilator bearing is verified.

关 键 词：煤矿主通风机 轴承 多尺度字典学习 故障诊断 振动监测 

分 类 号：TD441[矿业工程—矿山机电]