小波相关特征尺度熵在滚动轴承故障诊断中的应用

将小波相关滤波方法与Shannon信息熵相结合,提出了一种故障检测与诊断的方法——小波相关特征尺度熵故障法。首先利用小波相关滤波方法提取滚动轴承故障振动信号的微弱故障信息特征,以求得信噪比较高的尺度域小波系数;然后结合Shannon信息熵理论给出了沿尺度分布的小波相关特征尺度熵定义及其计算方法。小波相关特征尺度熵能够定量表征不同尺度的能量分布,各尺度能量分布的均匀性可以反映滚动轴承的运行状态的差别,选取最能反映故障特征的小波相关特征尺度熵作为特征参数,通过所选取的小波相关特征尺度熵大小判断滚动轴承的工作状态和故障类型。实验证明该方法能有效地判断滚动轴承故障特征,为滚动轴承故障诊断提供了新的思路。

Application of Wavelet Correlation Feature Scale Entropy to Fault Diagnosis of Roller Bearings

A method of the fault detection and diagnosis by combining wavelet correlation filter and Shannon information entropy was proposed. The method was called "wavelet correlation feature scale entropy-fault".At first,the weak fault information features were picked up from roller bearings fault vibration signals by the way of wavelet transform correlation filter,in order to get high signal to noise scales wavelet coefficients.Then,the defining and computing way of wavelet correlation feature scale entropy was presented, based on the integration of shannon information entropy theory.Wavelet correlation feature scale entropy can quantitatively describe energy distributing of different scales which reflects difference of roller bearings running state,the work states and fault types were estimated by magnitude of the selected wavelet correlation feature scale entropy which can mostly embody fault features.Results and analysis of the diagnosing example reveal that the proposed method can effectively estimate roller bearings fault feature and offer one new way for diagnosing roller bearings.