EWT-多尺度模糊熵-VPMCD融合算法的轴承故障识别分类应用

Bearing Fault Recognition and Classification Method based on EWT and Multiscale Fuzzy Entropy and VPMCD

针对振动信号提取轴承故障特征及识别分类的研究方式,提出了一种结合EWT-多尺度模糊熵-VPMCD的方法。首先,运用经验小波变换提取振动信号的模态分量。其次,引入信息论中的模糊熵算法,并加以多尺度粗粒度划分得到多尺度模糊熵特征描述。然后,用VPMCD对特征向量进行自适应选择预测模型训练。最终通过实验表明:模态分量多尺度模糊熵能够有效描述故障特征;VPMCD在少训练样本情况下获得了最低90%的分类准确率,相较一些常用的分类方法有着更好的性能表现。

Aiming at the research methods of extracting bearing fault characteristics,identifying and classifying vibration signals,a new method combining EWT、multi-scale fuzzy entropy and VPMCD algorithms is proposed in the thesis.Firstly,the modal components of the vibration signal are extracted by the empirical wavelet transform(EWT).Secondly,the fuzzy entropy algorithm in information theory is introduced,and multi-scale coarse-grained partitioning method is used to obtain the multi-scale fuzzy entropy feature description.Then,VPMCD algorithm is used to train the model by adaptively selecting prediction model.The experiments show that multi-scale fuzzy entropy of modal components can effectively describe the fault features.VPMCD achieves a minimum classification accuracy of 90%with a small number of training samples,which has better performance than some common classification methods.