| 康守强,叶立强,王玉静,谢金宝,Mikulovich V I.基于MCEA-KPCA和组合SVR的滚动轴承剩余使用寿命预测[J].电子测量与仪器学报,2017,31(9):1365-1371 |
| 基于MCEA-KPCA和组合SVR的滚动轴承剩余使用寿命预测 |
| Remaining useful life prediction of rolling bearing based on MCEA-KPCA and combined SVR |
| |
| DOI：10.13382/j.jemi.2017.09.003 |
| 中文关键词: 滚动轴承 有效性分析 特征约简 剩余使用寿命预测 |
| 英文关键词:rolling bearing effectiveness analysis feature reduction remaining useful life prediction |
| 基金项目:国家自然科学基金（51305109）、黑龙江省青年科学基金（QC2014C075）、哈尔滨理工大学青年拔尖创新人才培养计划（201511）资助项目 |
|
|
| Author | Institution |
| Kang Shouqiang | School of Electrical and Electronic Engineering, Harbin University of Science and Technology, Harbin 150080, China |
| Ye Liqiang | School of Electrical and Electronic Engineering, Harbin University of Science and Technology, Harbin 150080, China |
| Wang Yujing | School of Electrical and Electronic Engineering, Harbin University of Science and Technology, Harbin 150080, China |
| Xie Jinbao | School of Electrical and Electronic Engineering, Harbin University of Science and Technology, Harbin 150080, China |
| Mikulovich V I | Belarusian State University, Minsk 220030, Belarus |
|
| 摘要点击次数: 2427 |
| 全文下载次数: 7238 |
| 中文摘要: |
| 为了准确预测滚动轴承的剩余使用寿命(RUL)，提出一种多评价标准有效性分析(MCEA)、核主成分分析(KPCA)和组合支持向量回归(SVR)相结合的滚动轴承RUL预测方法。该方法对提取的特征计算每个评价标准的有效性得分，自适应地确定每个评价标准的权重，筛选出有效性总得分高于其整体平均值的特征，进一步利用KPCA去除已筛选特征之间的信息冗余，建立约简后的特征矩阵。将多个轴承约简后的特征分别作为SVR的输入，当前使用寿命与全寿命的比值p即RUL作为输出，建立多个SVR模型，并采用自适应的方法确定各模型的权重，最终构建组合SVR预测模型。最后，对与训练不同的轴承进行测试，将约简后特征输入到组合SVR预测模型中，预测轴承的p值，实验结果表明，所提方法可准确地对滚动轴承进行RUL预测。 |
| 英文摘要: |
| In order to predict the remaining useful life (RUL) of rolling bearing accurately, a RUL prediction method of rolling bearing based on multiple criterions effectiveness analysis (MCEA), kernel principal component analysis (KPCA) and combined support vector regression machine (SVR) is proposed. For extracted feature, the effectiveness score of each criterion can be calculated, and the weight of each criterion can be determined adaptively, the feature will be sifted when the effectiveness total score of which is greater than its overall average, and the KPCA is used to remove the information redundancy among the sifted features, then the reduced feature matrix is established. The reduced features of multiple bearings are used respectively as the input of the SVR, the ratio p of the bearing running time to the whole life time, namely RUL are used as the output, multiple SVR models are established. And the self adaptive method is used to determine the weight of each SVR model, the combined SVR prediction model can be established at last. Finally, the bearing which is different from the training process is used for testing, the reduced features are used as the input of the combined SVR prediction model, the p value of the bearing is predicted. The experimental results show that the RUL of the rolling bearing can be predicted accurately by the proposed method. |
| 查看全文 查看/发表评论 下载PDF阅读器 |
|
|
|
