基于改进FOA优化的CS-SVM轴承故障诊断研究  

Bearing fault diagnosis based on a modified CS-SVM model optimized by an improved FOA algorithm

作　　者：何大伟 彭靖波[1] 胡金海[1] 李腾辉 贾伟州 HE Dawei;PENG Jingbo;HU Jinhai;LI Tenghui;JIA Weizhou(Institute of Aerospace Engineering,Air Force Engineering University,Xi’an 710038,China)

机构地区：[1]空军工程大学航空航天工程学院,西安710038

出　　处：《振动与冲击》2018年第18期108-114,共7页Journal of Vibration and Shock

基　　金：国家自然科学基金(51506221;51606219);陕西省自然科学基础研究计划(2015JQ5179)

摘　　要：针对故障诊断中的小样本及样本类不平衡问题。建立基于代价敏感支持向量机(CS-SVM)的故障诊断模型,提出采用改进FOA算法(IFOA)对规则化常数C+,C-和核函数参数g进行优化选取,通过增大对故障类样本错分的惩罚代价,提升对故障类的诊断正确率;以IMS航空轴承试验数据为对象,结合随机共振、KPCA特征提取方法对所提IFOA优化的CS-SVM模型进行了验证。结果表明,该方法能有效处理误分类代价不同的轴承故障诊断问题,提高了故障类样本的诊断正确率,可拓展应用至其它故障诊断领域。Aiming at the problems of small samples and dataset imbalance in fault diagnosis,an optimized cost-sensitive support vector machine(CS-SVM)model was proposed and an improved fly optimization algorithm(FOA)was applied to select the best regularized constants C+,C-,and the kernel function parameter g,by increasing the penalties for the misclassification of fault dataset,so as to raise the diagnostic accuracy of the fault samples.The proposed method was verified by combining the stochastic resonance and KPCA feature extraction methods,taking the experimental data of a IMS aviation bearing as an example.The results show that the CS-SVM can effectively deal with unbalanced samples of small fault class in bearing fault diagnosis,and improve the diagnosis accuracy.It can also be further expanded to other fields of fault diagnosis.

关 键 词：轴承故障诊断 改进果蝇优化算法(IFOA) 代价敏感支持向量机(S-SVM) 

分 类 号：V232.2[航空宇航科学与技术—航空宇航推进理论与工程]