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以车辆滚动轴承故障诊断模型为基础,针对其轴承的特点,提出了一种小波包分析和SOM神经网络相结合的故障诊断方法。将该方法应用于车辆滚动轴承的故障诊断中,经过大量实测数据的分析与验证,能够有效地诊断出轴承的故障类型,为旋转机械的动态监测和故障诊断提供了新的参考,具有重要的理论和实际工程应用价值。
Based on the fault diagnosis model of vehicle rolling bearing, a fault diagnosis method based on wavelet packet analysis and SOM neural network is proposed according to its bearing characteristics. The method is applied to the fault diagnosis of vehicle rolling bearing. Through the analysis and verification of a large number of measured data, it can effectively diagnose the type of bearing failure and provide a new reference for the dynamic monitoring and fault diagnosis of rotating machinery. There is an important theory And practical engineering value.