滚动轴承的故障特征提取技术与方法研究

基于小波分析获取轴承故障频率,并以该频率为中心点向前2.5 Hz和向后2.5 Hz的频带变化作为滚动轴承的特征值。通过归一化处理,将轴承内圈、外圈和滚动体的故障频带的能量作为故障特征参量,建立起故障频带能量与滚动轴承状态的映射关系,进而应用神经网络进行故障诊断,有效提高了轴承故障诊断的精确度

Rolling Bearing Fault Feature Extraction Technique and Method

Based on wavelet analysis for bearing fault frequencies,and to the frequency for the center forward and backward 2.5 Hz bands 2.5 Hz change as bearing the characteristics of the value.By normalizing,the bearing inner ring,outer ring and rolling bodies of the failure frequency band energy as the fault feature parameters and establish a rolling bearing fault frequency energy and the state mapping,and thus application of neural network fault diagnosis,and effectively raise the bearing troubleshooting.