基于部分集成局部特征尺度分解与拉普拉斯分值的滚动轴承故障诊断模型

提出了一种基于部分集成局部特征尺度分解(Partly ensemble local characteristic-scale decomposition,PELCD)、拉普拉斯分值(Laplacian score,LS)特征选择和基于变量预测模型模式分类(Variable predictive model based class discrimination,VPMCD)的滚动轴承故障诊断模型。PELCD是新提出的一种基于噪声辅助数据分析方法,克服了局部特征尺度分解的模态混淆问题,与传统的基于噪声辅助数据分析方法相比有一定的优越性,论文将其应用于滚动轴承振动信号的预处理。之后提取振动信号PELCD分量的时域和频域统计特征及振动信号的时频联合域特征;同时为了降低特征向量维数,提高诊断效率,采用LS优化特征向量。再将优化的特征向量输入到VPMCD分类器进行训练和测试。滚动轴承实验数据分析结果表明该模型能够有效地诊断故障程度和故障类型。

Fault Diagnosis Model for Rolling Bearing Based on Partly Ensemble Local Characteristic-scale Decomposition and Laplacian Score

A new rolling bearing fault diagnosis model is proposed based on partly ensemble local characteristic-scale decomposition (PELCD), Laplacian score (LS) for feature selection and variable predictive model-based class discrimination (VPMCD). PELCD overcame the mode mixing of LCD and excelled the traditional ensemble noise-assisted method. In this paper it is employed to deal with the rolling bearing vibration signal. Then the features in time and frequency domains of comments are extracted. Besides, LS is utilized for feature selection to decrease the dimension of feature vector and improve the efficiency of fault diagnosis. The selected features are inputted to the VPMCD-based classifier for training and testing. Finally, the proposed model for rolling bearing fault diagnosis is applied to experimental data and the analysis results indicate that the proposed method can identify fault categories and degree effectively.