基于遗传规划和线性鉴别分析的故障特征提取模型及其应用

Feature extraction based on genetic programming and linear discriminant analysis for fault diagnosis and its application

关键词:  航空发动机  故障诊断  特征提取+  遗传规划+  线性鉴别分析+

Key words:  Aeroengine  Fault diagnosis  Feature extraction~+  Genetic programming~+  Linear discriminant analysis~+

摘要:

提出了一种基于遗传规划和线性鉴别分析的故障特征提取模型。该模型首先利用遗传规划从原始特征集中提取更能反映故障本质的复合特征,然后通过线性鉴别分析进行二次特征变换,消除特征之间的相关性以及压缩特征维数,得到对分类识别更有效、数目更少的特征。通过航空发动机滑油系统故障识别实验,表明经过遗传规划和线性鉴别分析提取的特征对故障具有更好的识别能力,并且对分类器具有很强的鲁棒性。

Abstract:

A new feature extraction model based on genetic programming and linear discriminant analysis in fault diagnosis is proposed.In this model,genetic programming is first used to construct compound features from original feature set.Then linear discriminant analysis is employed to get rid of the correlation among features and reduce the dimension of features.Thus the more effective and smaller subset of features for classification can be gained.Fault recognition experiments in aeroengine lubricating oil system are carried out to test the performance of this model.Practical results show that the extracted features based on genetic programming and linear discriminant analysis have better recognition ability and they are robust for various classifiers.