基于细菌觅食优化决策的齿轮箱故障诊断  

Gearbox fault diagnosis based on bacterial foraging algorithm optimization decisions

作　　者：杨大炼[1] 刘义伦[1,2] 李学军[3] 周维[1] 

机构地区：[1]中南大学机电工程学院,湖南长沙410083 [2]中南大学轻合金研究院,湖南长沙410083 [3]湖南科技大学湖南省机械设备健康维护重点实验室,湖南湘潭411201

出　　处：《中南大学学报（自然科学版）》2015年第4期1224-1230,共7页Journal of Central South University:Science and Technology

基　　金：国家自然科学基金资助项目(51375500);湖南省教育厅资助项目(2013SK2001);湖南省科技计划项目(2012GK3171);中南大学中央高校基本科研业务费专项资金资助项目(2013zzts037)~~

摘　　要：在利用SVM对齿轮箱进行故障诊断决策时,SVM模型参数(核参数及惩罚因子)对齿轮箱故障的诊断结果影响很大,而最优参数难以获取,针对这一问题,提出一种基于自适应细菌觅食算法(BFA)的SVM参数快速选取方法。以齿轮箱故障诊断过程为实验对象,对比分析网格搜索法、遗传算法、粒子群算法与细菌觅食算法对SVM径向基核函数参数δ及惩罚因子C的优化性能。研究结果表明:细菌觅食算法能够更加快速地选取到最优参数;采用细菌觅食算法优化SVM参数可以进一步提高齿轮箱故障诊断的精度。Using SVM to study the fault diagnosis decisions for gearboxes, the SVM model parameters（kernel parameters and penalty factor） have great influence on the gear fault diagnosis results, however, the best parameters are difficult to obtain. In order to solve this problem, a rapid selection method was proposed for SVM based on adaptive bacterial foraging algorithm（BFA）. By taking the gearbox fault diagnosis process as experimental subject, the performance of the grid search method, genetic algorithm, particle swarm optimization and bacterial foraging algorithm was analyzed to optimize the RBF kernel function＇s parameter δ and the penalty factor C. The results show that the bacterial foraging algorithm can select more quickly the optimal parameters. Using the bacterial foraging algorithm to optimize SVM can further improve the accuracy.

关 键 词：细菌觅食算法 支持向量机 参数优化 齿轮箱 故障诊断 

分 类 号：TP206.3[自动化与计算机技术—检测技术与自动化装置]