基于多核超球支持向量机的滚动轴承状态定量评估方法研究

项目名称： 基于多核超球支持向量机的滚动轴承状态定量评估方法研究

项目编号： No.51305109

项目类型： 青年科学基金项目

立项/批准年度： 2014

项目学科： 机械、仪表工业

项目作者： 康守强

作者单位： 哈尔滨理工大学

项目金额： 26万元

中文摘要： 研究滚动轴承不同故障类型（内环、外环、滚动体）及不同性能退化程度的多状态智能定量评估方法，其结果可更准确地揭示滚动轴承性能退化的规律，具有重要科学意义。提出在改进集合经验模态分解（MEEMD）算法中加入带限白噪声的幅值系数确定方法、MEEMD所得的固有模态函数提存的改进算法，并利用流形学习对多种特征提取方法所得特征向量进行约简；采用组合多种核函数的方式优化单核超球支持向量机，建立新的多核映射的空间距离函数。在此基础上推导多核映射的超球球心间的距离公式，以此作为分离指数确定多核分类器核参数的最优选取范围，进一步优化分类器；提出多核映射的最小空间距离准则、模糊隶属度函数、多核映射的空间距离函数相结合的方法，建立滚动轴承多状态评估模型，智能定量评估滚动轴承的状态。通过理论和实验研究相结合的手段，验证该评估方法的有效性，为实现滚动轴承及其它设备的主动维护奠定理论基础。

中文关键词： 特征提取；超球支持向量机；多核学习；滚动轴承；状态智能评估

英文摘要： A multi-condition intelligent quantitative assessment method is studied for different fault types (inner raceway, outer raceway, rolling element) and different degrees of performance degradation of rolling bearing. The study result can show the performance degradation law of rolling bearing more accurately and has important scientific significance. The amplitude coefficient determined method is proposed for band-limited white noise that added to modified ensemble empirical mode decomposition (MEEMD) algorithm, the improved extracting sensitive component method for intrinsic mode function (IMF) obtained by MEEMD is proposed. The feature vectors obtained by several feature extraction methods are reduced by using manifold learning. Single kernel hypersphere support vector machine is optimized by combining with multiple kernel functions, and new spatial distance function of multi-kernel mapping is defined. On the basis of above results, the distance formula of hypersphere spherical centers is derived for multi-kernel mapping, and is regarded as separation index to determine the optimal kernel parameter selection range of multi-kernel classifier for further optimizing the classifier. Combining the minimum spatial distance rule of multi-kernel mapping with fuzzy membership function and spatial distance function of mul

英文关键词： feature extraction；hypersphere support vector machine；multi-kernel learning；rolling bearing；condition intelligent assessment