基于SVD-LMD模糊熵与PNN的滚动轴承故障诊断

Fault Diagnosis of Bearing based on SVD-LMD Fuzzy Entropy and PNN

为了提高轴承故障特征信息提取的有效性,实现轴承故障模式智能识别,提高故障诊断效率。提出一种基于SVD-LMD模糊熵相结合的特征量化和PNN网络识别相结合的滚动轴承故障诊断方法。首先运用SVD降噪技术对原始信号降噪,运用LMD分解将降噪后的非稳定信号分解成若干个稳定的乘积函数分量(PF)。其次利用模糊熵能表征时间序列复杂程度并具有稳定的统计性,提取PF分量的模糊熵,组成N维特征向量,实现故障特征量化。构建PNN网络模型,将特征向量输入PNN训练,实现故障类型识别。最后对比PNN算法与BP算法性能,验证PNN算法的优越性。实验数据分析结果表明,所提方法在少量数据样本情况下故障诊断准确率高达93.75%。

In order to improve the effectiveness of bearing fault feature information extraction,the intelligent recognition of bearing fault pattern is realized,and the efficiency of fault diagnosis is improved. The method of rolling bearing fault diagnosis is proposed,which combined with de- noising of singular value decomposition and fuzzy LMD feature quantification and PNN neural network recognition. In this method,firstly,the original signal is reduced by using SVD noise reduction technique,and the LMD decomposition is used to decompose the non- stationary signal into a number of stable product function components（ PF）. Secondly,because the fuzzy entropy can be used to characterize the complexity of time series and the stability of statistical property. The fuzzy entropy of PF component is extracted to form N dimension feature vector. The PNN network model is constructed,the feature vector is input PNN model to realize the fault type identification. Finally,the performance of the PNN algorithm and BP algorithm are compared,and the superiority of the PNN algorithm is shown. The experimental data show that the proposed method has a high accuracy rate of 93. 75% in the case of a small number of data samples.