| 罗 亭,王晓东,马 军,杨创艳.基于 ICFE 和 WPHM 的滚动轴承健康状态评估[J].电子测量与仪器学报,2021,35(12):116-125 |
| 基于 ICFE 和 WPHM 的滚动轴承健康状态评估 |
| Health assessment of rolling bearing based on ICFE and WPHM |
| |
| DOI： |
| 中文关键词: 改进的交叉模糊熵 威布尔比例故障率模型 健康状态 滚动轴承 |
| 英文关键词:improved cross fuzzy entropy Weibull proportional failure rate model health status rolling bearing |
| 基金项目:国家自然科学基金（51765022,61663017）、云南省科技厅科技计划项目（2019FD042）资助 |
|
|
| Author | Institution |
| Luo Ting | 1. Faculty of Information Engineering and Automation, Kunming University of Science and Technology,2. Yunnan Key Laboratory of Artificial Intelligence, Kunming University of
Science and Technology |
| Wang Xiaodong | 1. Faculty of Information Engineering and Automation, Kunming University of Science and Technology,2. Yunnan Key Laboratory of Artificial Intelligence, Kunming University of
Science and Technology |
| Ma Jun | 1. Faculty of Information Engineering and Automation, Kunming University of Science and Technology,2. Yunnan Key Laboratory of Artificial Intelligence, Kunming University of
Science and Technology |
| Yang Chuangyan | 1. Faculty of Information Engineering and Automation, Kunming University of Science and Technology,2. Yunnan Key Laboratory of Artificial Intelligence, Kunming University of
Science and Technology |
|
| 摘要点击次数: 1416 |
| 全文下载次数: 46739 |
| 中文摘要: |
| 针对滚动轴承振动信号的非线性动态特性及可靠度评估精度不高的问题,提出了基于改进的交叉模糊熵( improved
cross fuzzy entropy,ICFE)和威布尔比例故障率模型(Weibull proportional hazards model,WPHM)的滚动轴承健康状态评估方法。
该方法首先对原始振动信号进行改进的微分局部均值分解(Crt-differential local meandecomposition,Crt-DLMD),选取包含故障
信息最多的有效分量进行重构;然后,利用滑动均值取代原有粗粒化过程,计算重构信号的 ICFE;最后,将 ICFE 作为 WPHM 的
协变量进行健康状态评估。 通过美国国家航空航天局(NASA)和西安交通大学-长兴昇阳科技有限公司的滚动轴承全寿命周期
数据实验表明,所提方法可以准确、有效地评估滚动轴承的健康状态。 |
| 英文摘要: |
| In view of the nonlinear dynamic characteristics of rolling bearing vibration signal and the low accuracy of reliability
evaluation, a rolling bearing health condition assessment method based on improved cross fuzzy entropy (ICFE) and Weibull proportional
hazards model (WPHM) was proposed. Firstly, the original vibration signal is decomposed by improved DLMD (Crt- DLMD), and the
effective component with the most fault information is selected for reconstruction. Then, the ICFE of the reconstructed signal is calculated
by using the sliding mean instead of the original coarse-grained process. Finally, the ICFE is used as the covariate of WPHM for health
status assessment. The life cycle data and experiments of rolling bearing from national aeronautics and space administration (NASA) and
Xi′an Jiaotong University Changxing Shengyang technology (XJTU-SY) show that the proposed method can accurately and effectively
evaluate the health status of rolling bearings. |
| 查看全文 查看/发表评论 下载PDF阅读器 |
|
|
|
