基于遗传编程和支持向量机的故障诊断模型

提出了一种基于遗传编程和支持向量机的故障诊断模型．该模型利用遗传编程对传统的时域指标进行特征选择和提取，得到更能反映信号本质的特征，与其他特征组合后作为识别特征输入多类支持向量机，实现了对机器不同类型故障的识别．实验结果表明，同传统时域指标相比，经过遗传编程选择和提取的特征对轴承的故障具有更好的识别能力，进而提高了多类支持向量机的分类准确性．

Fault Detection Based on Genetic Programming and Support Vector Machines

A new classification model based on genetic programming and support vector machine for machine fault diagnosis was proposed. In this model, genetic programming constructs and selects features from original feature set. The selected features form input feature set of support vector machines. Then multi-class support vector machine is applied to detect abnormal cases from normal ones. Experiments of rolling bearings fault detection are carried out to test the performance of this model. Practical results show that the compound features generated by genetic programming possess better recognition ability than the initial time domain features do. The classification ability of multi-class support vector machine is improved after feature extraction and selection.