基于正交邻域保持嵌入特征约简的故障诊断模型

Fault diagnosis model based on feature compression with orthogonal neighborhood preserving embedding

提出一种基于正交邻域保持嵌入(orthogonal neighborhood preserving embedding,ONPE)特征约简的故障诊断模型。首先将原振动信号经验模式分解(empirical mode decomposition,EMD)并构造Shannon熵得到高维特征向量,再利用ONPE将高维特征向量约简为低维特征向量,并输入到最近邻分类器(k-nearest neighbors classifier,KNNC)中进行故障识别。本模型充分利用了EMD分解在故障特征提取、ONPE在信息压缩和KNNC在分类决策方面的优势,实现了旋转机械故障特征提取到故障诊断的全程自动化,并提高了诊断精度,为旋转机械故障诊断提供了一种新的模型分析方法。一个滚动轴承故障诊断实例验证了该模型的有效性。

A new fault diagnosis model is proposed based on feature compression with Orthogonal Neighborhood Preserving Embedding（ONPE）.Firstly,the fault vibration signals are decomposed using EMD and Shannon entropy is constructed to get high-dimensional eigenvectors;then the high-dimensional eigenvectors are compressed to low-dimensional eigenvectors with ONPE;finally,the low-dimensional eigenvectors are inputted into K-nearest neighbor classifier（KNNC） for fault classification.Making full use of the advantages of EMD decomposition in fault feature extraction,ONPE in information compression and KNNC in classification decision-making,the proposed model not only realizes complete automation from fault feature extraction to fault diagnosis,but also improves accuracy of fault diagnosis;and also provides a new model analysis method for rotating machinery fault diagnosis.A rolling-bearing fault diagnosis example proves the effectivity of this new model.