基于免疫聚类分析的特征提取及其在发动机故障诊断中的应用

以提高航空发动机故障诊断的快速性和准确性为目的，基于人工免疫理论中的克隆选择算法，结合聚类分析方法，提出了基于免疫聚类分析的故障特征提取方法。该方法通过删除对分类无关的特征以及压缩类间相关特征，得到最有利于分类的子特征集，提高了分类器的分类性能。并且该算法具有本质上的并行性、计算效率高和聚类能力强等优点。多类支持向量机的分类实验表明，经过基于免疫聚类分析提取的特征对发动机的故障具有更好的识别能力，为发动机的状态监测与故障诊断提供了依据。

Feature extraction based on immune clustering analysis and its application in aeroengine fault diagnosis

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 muhi - 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, fault diagnosis.