关键词: 航空发动机;故障诊断;特征提取;免疫聚类分析;克隆选择算法

Abstract: In order to improve the rapidity and validity of aeroengine fault diagnosis,a novel approach based on clonal selection algorithm and combined with clustering analysis was proposed for aeroengine fault feature extraction.This data analysis approach can not only reduce the dimension of features by getting rid of the correlation among them but also remove the duplicated or proximately similar data. The obtained subset of features can reduce the cost of computation during the classification process,while improving classifier efficiency.And the method has the essential advantages of high parallel,high efficiency of computation and good clustering ability.Experiments of multi-class support vector machine classifier were carried out to test the performance of this method.Practical results show that the extracted features based on immune clustering analysis perform better recognition ability for aeroengine fault.Therefore,it lays a sound foundation for engine condition detection and fault diagnosis.

Key words: Aircraft engine;Fault diagnosis;Feature extraction;Immune clustering analysis;Clonal selection algorithm