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

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.