基于EMD-1(1/2)维谱熵的滚动轴承故障诊断

为了准确地诊断出滚动轴承的运行状态,将1(1/2)维谱熵引入滚动轴承故障诊断中。先对滚动轴承原始故障信号进行EMD(empirical mode decomposition)分解得到若干个固有模态函数(intrinsic mode function,IMF),进而再求取各个IMF的1(1/2)维谱熵值,作为表征滚动轴承故障类型的特征向量。将其作为Elman神经网络的输入参数,最后区分滚动轴承故障状态和故障类型。仿真分析和实验研究表明,该方法能够有效地提取出滚动轴承的故障特征,最后通过与小波包分析-BP神经网络故障诊断方法对比,显示出其具有更高的识别率,更加表明其可行性和有效性。

Fault Diagnosis of Rolling Bearings Based on EMD 1(1/2)-Dimensional Spectral Entropy

This paper presents a method to diagnose the faults of rolling bearings accurately, into which the 1(1/2)-dimensional spectral entropy is introduced. First, we decompose the empirical mode decomposition (EMD) and calculate the 1(1/2)-dimensional spectral entropy value by the intrinsic mode functions (IMF) we get. Second the value is input to the Elman Neural Network as a new eigenvector to characterize the fault type of the rolling bearing. Then we can distinguish the fault status and fault type of the rolling bearing. Simulation analysis and experimental study show that this method can effectively extract the fault features of rolling bearings. Compared with the Wavelet Packet Analysis-Neural Network fault diagnosis, this method is more feasible and effective with a higher recognition rate.