矿用车辆变速箱圆柱滚子轴承故障诊断

Fault Diagnosis of Cylindrical Roller Bearing of Mine Gearbox

矿用车辆变速箱的运行状态判断主要依靠人工拆卸、观察的方式,检测诊断的效率低下,甚至隐含着较大的安全隐患。以矿用车辆变速箱圆柱滚子轴承为研究对象,利用小波包对轴承故障振动信号进行分解,区分比较不同状态信号的总能量熵,利用支持向量机原理对矿用车辆变速箱圆柱滚子轴承故障进行诊断。试验表明,应用SVM和小波包能量熵可以对轴承故障进行有效诊断,且准确率较高,研究对矿用车辆变速箱圆柱滚子轴承的故障诊断有着现实的指导意义。

Mine vehicle transmission status judgments rely mainly on manual demolition, observation,detection and diagnosis of inefficient, and even implied a greater security risks. The cylindrical roller bearing of the mine vehicle gearbox is taken as the research object. The wavelet packet is used to decompose the vibration signal of the bearing fault. The total energy entropy of the different state signals is compared and analyzed. The principle of the support vector machine is used to mine the roller bearing failure to diagnose. The experimental results show that SVM and wavelet packet entropy can effectively diagnose the bearing failure and have high accuracy. This study has practical significance for the fault diagnosis of cylindrical roller bearing of mine vehicle.