基于Levy-ABC优化SVM的水电机组故障诊断方法  

Fault Diagnosis for Hydroelectric Generator Unit Based on Levy Flight-Artificial Bee Colony and Support Vector Machine

作　　者：肖剑[1] 周建中[1] 张孝远[1] 李超顺[1] 寇攀高[1] 肖汉[1] 

机构地区：[1]华中科技大学水电与数字化工程学院,武汉430074

出　　处：《振动．测试与诊断》2013年第5期839-844,914,共6页Journal of Vibration,Measurement & Diagnosis

基　　金：国家自然科学基金资助项目(51079057;51039005);高等学校博士学科点专项科研基金资助项目(20100142110012)

摘　　要：水电机组故障诊断实质上是一种典型的小样本机器学习问题,支持向量机作为一种先进的机器学习方法,在解决小样本问题上有着突出的表现,但其参数设置问题一直未能很好的解决。针对此问题,提出了一种基于人工蜜蜂群优化支持向量机的水电机组故障诊断方法,为改进人工蜜蜂群全局搜索能力,引入Levy飞行策略,对原始人工蜜蜂群算法进行改进。实验表明,Levy飞行蜜蜂群优化和支持向量机相结合的水电机组故障诊断算法,对多类故障能够有效地分类,提高了水电机组故障诊断的准确率,具有一定的工程应用价值。Fault diagnosis of hydroelectric generating set is essentially a typical machine learning problem with small sample.As an advanced machine learning methods with outstanding performance on solving the small sample size problem,support vector machine has been employed in many fields.Nevertheless,the parameter selection of support vector machine has been remained as a problematic issue.A novel variant of artificial bee colony is proposed by introducing the levy flight strategy to tackle the parameter selection of support vector machine,and the LABC-SVM is employed in the fault diagnosis of hydropower units.The numerical simulation results show that the combination of artificial bee colony with levy flight and support vector machine can be applied to multi-class diagnosis of hydroelectric generating set and efficaciously improved the accuracy of classification,thus it is valuable for engineering.

关 键 词：人工蜜蜂群算法 水电机组 故障诊断 支持向量机 参数优化 

分 类 号：TM6[电气工程—电力系统及自动化] TP307[自动化与计算机技术—计算机系统结构]