最优最小熵反褶积与包络-导数能量算子在轴承故障提取中的应用

Application of optimal minimum entropy deconvolution and envelope-derivation energy operator in bearing fault extraction

最小熵反褶积是检测轴承故障或齿轮故障信号等类脉冲信号的一种有效技术,但是该方法仍存在一个不足,即在使用前须设置滤波器的长度,而该参数值的选择一般只能通过技术人员的经验选择。针对这个局限性,提出了一个基于峭度、排列熵与信号能量的滤波器长度选择准则。通过该准则,可以有效地挑选出最优的滤波器长度,从而更好地对故障信号进行滤波。随后,一种增强的能量算子,包络-导数能量算子用来对过滤后的故障信号进行故障特征频率的提取。实验结果表明,该方法不仅可以有效地提取出轴承故障特征频率,并且与一些传统方法相比,该方法可以大大突出故障特征频率的幅值。

Minimum entropy deconvolution(MED)is an effective technique for detecting impulse-like signals such as bearing fault or gear fault signal,but there is still a deficiency in this method,that is,a parameter of the filter length in this method has to be set before using.Unfortunately,the selection of this parameter value can only be chosen through the human experience.In order to overcome this limitation,an optimal selection indicator based on Kurtosis,permutation entropy(PE)and signal energy is proposed in this study.By virtue of this indicator,the optimal filter length can be selected to filter the raw signal better.Then,an enhanced energy operator named envelope-derivation energy operator(EDEO)is used to extract the fault characteristic frequency from the filtered signal.The experimental results show that,compared with the conventional methods,this proposed method can effectively extract the bearing fault characteristic frequency under harsh working conditions and obviously highlight the amplitude of the bearing fault frequency.