基于小波神经网络的煤矿提升机轴承故障诊断  

Fault Diagnosis of Coal Mine Hoist Bearing Based on Wavelet Neural Network

作　　者：郑勇[1] 钱正春 杨小兰[1] Zheng Yong;Qian Zhengchun;Yang Xiaoian(School of Mechanical.Nanjing Institute of Technology,Nanjing 211167,China)

机构地区：[1]南京工程学院机械学院,南京211167

出　　处：《煤矿机械》2021年第3期177-179,共3页Coal Mine Machinery

基　　金：国家自然科学基金青年基金(52005246)。

摘　　要：轴承故障严重地影响设备的使用寿命、效率及安全性。以某型煤矿提升机轴承为研究对象,采集煤矿提升机轴承实时数据,基于小波神经网络建立煤矿提升机轴承故障诊断模型,运用MATLAB对煤矿提升机小波神经网络模型进行数值计算,对煤矿提升机轴承故障进行诊断。诊断结果表明,不同运行时间下,煤矿提升机轴承振动信号不同;基于小波神经网络可以精确、高效对煤矿提升机轴承故障进行诊断。该研究为煤矿提升机轴承故障诊断、维修等方面提供理论依据。Bearing fault greatly affects the service life, efficiency and safety of the equipment. A certain type of coal mine hoist bearing was taken as the research object, the real-time data of coal mine hoist bearing were collected, a coal mine hoist bearing fault diagnosis model based on wavelet neural network has been established, and the wavelet neural network model of coal mine hoist has been numerically calculated by MATLAB to diagnose the fault of coal mine hoist bearing. The diagnosis results show that the bearing vibration signals of coal mine hoists are different under different operating hours;the wavelet neural network can accurately and efficiently diagnose coal mine hoist bearing faults.The research provides theory for coal mine hoist bearing fault diagnosis and maintenance.

关 键 词：小波神经网络 煤矿提升机 轴承 故障诊断 

分 类 号：TD534[矿业工程—矿山机电]