摘要
提出了一种基于支持向量回归的齿轮箱故障诊断方法。通过提取能反映齿轮箱工作状态的特征参数,并将分类问题转化为回归问题,针对性地构造了多分类支持向量回归决策机构并将其用于齿轮箱故障诊断,避免了投票决策机构等票数无法分类问题。相比于人工神经网络,该方法具有收敛速度快、泛化能力强的优点。
Gearbox is one of the most important components widely used in rotary machines and its health status is the key factor for the stable operation of the machinery.Hence,the condition monitoring and fault diagnosis of gearbox is of great significance.A new gearbox fault diagnosis method based on support vector regression is proposed.Firstly,the features that can reflect the health status of the gearbox are extracted,and then the problem of classification is transferred to regression problem.Finally,a new support vector regression decision mechanism is constructed and applied to the diagnosis of gearbox.It effectively avoids the problem of equal votes in voting decision organization.Comparing to Artifical Neural Network(ANN),the proposed method converges fast and has better generalization ability.
出处
《振动．测试与诊断》
EI
CSCD
北大核心
2013年第5期775-781,909,共7页
Journal of Vibration,Measurement & Diagnosis
基金
国家自然科学基金资助项目(51375322)
江苏省自然科学基金资助项目(BK2010225)
关键词
齿轮箱
特征提取
故障诊断
支持向量回归
gearbox
feature extraction
fault diagnosis
support vector regression
