基于经验小波变换和峭度值的滚动轴承故障检测方法

A novel rolling bearing fault detection method based on empirical wavelet transform and kurtosis value

机械故障信号大多具有复杂多分量和调幅-调频的特点,但目前已有的方法在实际分析中多存在缺陷。为了有效识别故障特征频率,该文提出了一种基于EWT和峭度值的轴承故障检测方法。这种方法通过EWT对滚动轴承的振动信号进行分解,得到多个调频分量(AM-FM),并用文中提出的计算方法得到的各个调频分量的特征指标后进行筛选,得到包含敏感故障信息的分量。将该方法应用于轴承故障信号的解调分析,提高了分析的针对性。将改进方法应用于轴承故障实测信号分析,验证了该方法的准确性。

Most of the mechanical fault signals have complex multi-component and amplitude modulation-frequency modulation characteristics,but most of the existing methods are flawed in practical analysis.In order to effectively identify the fault feature frequency,this paper presents a bearing fault detection based on EWT and kurtosis value method.This method decomposes the vibration signal of the rolling bearing through EWT to obtain a plurality of frequency-modulated components, and filters the characteristic indexes of each frequency-modulated component obtained by the calculation method proposed in the text, and obtains the component containing the sensitive fault information. This method is applied to the demodulation analysis of the bearing fault signal, which improves the pertinence of the analysis. The improved method was applied to analyze the measured signal of bearing failure, and the accuracy of the method was verified.