[1]胡启国,何奇,曹历杰.采用EEMD-KPCA处理的IHHO-LSSVM滚动轴承寿命预测模型[J].华侨大学学报(自然科学版),2022,43(2):145-153.[doi:10.11830/ISSN.1000-5013.202012025]
　HU Qiguo,HE Qi,CAO Lijie.IHHO-LSSVM Rolling Bearing Life Prediction Model Treated by EEMD-KPCA[J].Journal of Huaqiao University(Natural Science),2022,43(2):145-153.[doi:10.11830/ISSN.1000-5013.202012025]
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采用EEMD-KPCA处理的IHHO-LSSVM滚动轴承寿命预测模型()
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《华侨大学学报（自然科学版）》[ISSN:1000-5013/CN:35-1079/N]

卷:
第43卷
期数:
2022年第2期
页码:
145-153
栏目:
出版日期:
2022-03-08

文章信息/Info

Title:
IHHO-LSSVM Rolling Bearing Life Prediction Model Treated by EEMD-KPCA
文章编号:
1000-5013(2022)02-0145-09
作者:
胡启国1;  何奇1;  曹历杰2
1. 重庆交通大学 机电与车辆工程学院, 重庆 400074;2. 川庆钻探工程公司 安全环保质量监督检测研究院, 四川 广汉 618300
Author(s):
HU Qiguo1;  HE Qi1;  CAO Lijie2
1. School of Mechatronics and Vehicle Engineering, Chongqing Jiaotong University, Chongqing 400074, China; 2. Safety and Environmental Protection Quality Supervision and Inspection Institute, Chuanqing Drilling Engineering Company, Guanghan 618300, China
关键词:
滚动轴承;  剩余寿命预测;  集合经验模态分解;  哈里斯鹰优化算法;  最小二乘支持向量机;  核主成分分析
Keywords:
rolling bearing;  remaining life prediction;  ensemble empirical mode decomposition;  Harris hawk optimization algorithm;  least square support vector machine;  kernel principal component analysis
分类号:
TH133.33;TP18
DOI:
10.11830/ISSN.1000-5013.202012025
文献标志码:
A
摘要:
为提高滚动轴承剩余寿命预测精度,提出一种基于集合经验模态分解-核主成分分析(EEMD-KPCA)和改进的哈里斯鹰优化-最小二乘支持向量机(IHHO-LSSVM)的滚动轴承剩余寿命预测模型.首先,使用集合经验模态分解方法对原信号进行分解,根据相关系数和峭度值选取合适的本征模态函数进行重构;然后,提取时域、频域、小波包能量谱等指标,并用核主成分分析,选取累计贡献率大于85%的主成分作为轴承退化性能指标;建立最小二乘支持向量机寿命预测模型,针对模型参数,提出一种改进的哈里斯鹰优化算法,并在新算法基础上设计新的能量周期性递减调控机制.采用轴承全寿命实验数据进行验证,结果表明:该方法提取的轴承性能评估指标能够更全面地表征轴承性能退化情况,建立的模型具有良好的预测效果.
Abstract:
In order to improve the prediction accuracy of the remaining life of the rolling bearing,a model based on ensemble empirical mode decomposition-kernel principal component analysis(EEMD-KPCA)and improved Harris hawk optimization-least squares support vector machine(IHHO-LSSVM)was proposed. First, the EEMD method was used to decompose the original signal,and the appropriate eigenmode function was selected for reconstruction according to the correlation coefficient and kurtosis value. Then, the time domain and frequency domain indicators together wavelet packet energy spectrum, etc. were extracted, and using nuclear PCA, the principal component with a cumulative contribution rate greater than 85% was selected as the bearing degradation performance index. The LSSVM life prediction model was established, the parameters of which were optimized by a proposed IHHO algorithm. On the basis of the new algorithm,a new energy cyclical decrease regulation mechanism was designed. The rolling bearing full life experimental data was used for verification. The results show that the bearing performance evaluation index extracted by the proposed method can more comprehensively characterize the degradation of rolling bearing performance,and the established model has a good prediction effect.

参考文献/References:

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备注/Memo

备注/Memo:
收稿日期: 2020-12-12
通信作者: 胡启国(1966-),男,教授,博士,主要从事机械可靠性分析及优化、机械系统动力学的研究.E-mail:409500847@qq.com.
基金项目: 国家自然科学基金资助项目(51375519); 重庆市基础科学与前沿技术研究专项资助项目(cstc2015jcy jBX0133)http://www.hdxb.hqu.edu.cn
更新日期/Last Update: 2022-03-20