一种用于风机故障诊断的免疫克隆特征选择算法

提出一种新的用于风机故障诊断的免疫克隆特征选择算法.提取了生产线上实测风机噪声的时域波形结构特征、小波分析特征及听觉谱特征,进行特征选择和故障诊断仿真实验.实验结果表明：在特征选择后的特征数目比原特征数目减少61% 的情况下,支持向量机分类器的分类正确率下降很小,分类时间显著减少.实验结果证明了该算法的有效性和鲁棒性,且能有效地应用于风机故障诊断.

An immune clone feature selection algorithm for fan fault diagnosis

In this paper, a novel Immune Clone Feature Selection Algorithm (ICFSA) is proposed for fan fault diagnosis. The time wave structure features, wavelet analysis features and auditory spectrum features of real fan noise collected in the factory production line are extracted. The proposed method is compared with genetic algorithm in classification and feature selection experiments. The experimental results show that: (1) the classification accuracy of support vector machine classifier decreases a very little while the number of features is reduced 61% by the proposed method and the classification time is much shorter; (2) the proposed algorithm can converge to a more optimal feature subset faster than genetic algorithm. The results demonstrate that the proposed algorithm is an effective and robust feature selection method, and useful for fan fault diagnosis.