基于灵敏度分析的支持矢量机特征选择

特征选择可以去除冗余特征提高机械故障诊断精度和诊断效率。对于支持矢量机(SVM)作为故障决策器, 提出基于特征灵敏度分析的特征选择方法。此方法通过分析候选特征子集对SVM输出的影响大小,以此作为特征选择标准,并采用遗传算法搜索最佳特征子集。数值仿真和柴油机故障特征选择试验结果显示此方法有较好的寻优特征子集的能力,能够提高故障诊断的精度和效率。

SENSITIVITY ANALYSIS-BASED FAULT FEATURE SELECTION FOR SVM

Fault features set can be acquired by processing acquired data in machine diagnosis. Unfortunately many of these features are either partially or completely irrelevant or redundant to the fault state usually exist in the fault feature set. These features often decrease diagnosis accuracy and reduce the computational efficiency. Feature selection can remove those redundant features to avoid the influence. A new feature selection method on the basis of feature sensitivity analysis as selection evaluation is designed for support vector machine (SVM). Feature sensitivity is defined as relative feature impor- tance for the classification decision on the basis of a single SVM training run. And genetic algorithm is used for select optimization in the method. According to results of simulated data and diesel engine fault feature selection experiment, it is proved that this method possesses excellent optimization feature selection property, and acquires higher accuracy.