| 基于IMF能量矩和Bayes-LSSVM的滚动轴承诊断 |
| |
| 引用本文： | 邓辉,罗倩,王岩.基于IMF能量矩和Bayes-LSSVM的滚动轴承诊断[J].计算机工程与应用,2019,55(17):156-161. |
| |
| 作者姓名： | 邓辉 罗倩 王岩 |
| |
| 作者单位： | 北京信息科技大学 信息与工程学院,北京,100101;北京信息科技大学 信息与工程学院,北京,100101;北京信息科技大学 信息与工程学院,北京,100101 |
| |
| 基金项目： | 北京市科技提升计划项目;北京市教委科研计划项目 |
| |
| 摘 要： | 针对传统方法对滚动轴承故障特征提取效果尚有局限和最小二乘支持向量机分类器的参数不易确定，从而降低了故障诊断的准确性的问题，提出基于本征模函数能量矩和贝叶斯框架下的最小二乘支持向量机实现滚动轴承的故障诊断。在该方法中，通过经验模态分解将原始信号分解为多个本征模函数，之后将本征模函数作时间轴的积分，得到本征模函数能量矩特征故障向量。采用贝叶斯推理方法进行三级分层推断，解决最小二乘支持向量机分类器的参数具有任意性和不确定性的问题，实现参数优化。对滚动轴承的仿真结果表明，该方法能对故障进行有效、准确的诊断，诊断正确率达到98.75%。
|
| 关 键 词： | 故障诊断 经验模态分解 本征模函数能量矩 贝叶斯 最小二乘支持向量机 参数优化 |
Rolling Bearing Diagnosis Based on IMF Energy Moments and Bayes-LSSVM |
| |
| DENG Hui,LUO Qian,WANG Yan.Rolling Bearing Diagnosis Based on IMF Energy Moments and Bayes-LSSVM[J].Computer Engineering and Applications,2019,55(17):156-161. |
| |
| Authors: | DENG Hui LUO Qian WANG Yan |
| |
| Affiliation: | College of Information and Communication Engineering, Beijing Information Science and Technology University, Beijing 100101, China |
| |
| Abstract: | For the traditional method, the fault feature extraction effect of rolling bearings is still limited, and the parameters of the least squares support vector machine classifier are not easy to determine, which reduces the accuracy of fault diagnosis. Least squares support vector machine based on the energy moment of the eigenmode function and the Bayesian framework is proposed for fault diagnosis of rolling bearings. In this method, the original signal is decomposed into multiple eigenmode functions by empirical mode decomposition, and then the integral of the time axis is used as the eigenmode function to obtain the energy-moment feature fault vector of the intrinsic mode function. Bayesian inference method is used to perform three-level hierarchical inference and the parameters of the least squares support vector machine classifier have arbitrariness and uncertainty, and the parameters are optimized. The simulation results of the rolling bearing show that this method can effectively and accurately diagnose the fault, and the diag-nostic accuracy rate reaches 98.75%. |
| |
| Keywords: | fault diagnosis empirical mode decomposition energy moment of eigenmode function Bayesian least squares support vector machine parameter optimization |
| 本文献已被 万方数据 等数据库收录！ |
| 点击此处可从《计算机工程与应用》浏览原始摘要信息 |
|
点击此处可从《计算机工程与应用》下载全文 |
