采用显式动力学的轴承性能退化评估指标构建

张龙; 张号; 周建民; 彭小明; 王晓博; 乔宇

doi:10.7652/xjtuxb202208002

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采用显式动力学的轴承性能退化评估指标构建

专题智能诊断 | 更新时间：2025-07-09

- 采用显式动力学的轴承性能退化评估指标构建
- Construction of Bearing Performance Degradation Assessment Indicator Using Explicit Dynamics
- 西安交通大学学报 2022年56卷第8期页码：11-21
- 作者机构：
  
  华东交通大学载运工具与装备教育部重点实验室,南昌,330013
- 作者简介：
  
  : 2022-01-17。作者简介: 张龙(1980—),男,博士,副教授。基金项目: 国家自然科学基金资助项目(51865 010)
- 基金信息：
  
  : 2022-01-17。作者简介: 张龙(1980—),男,博士,副教授。基金项目: 国家自然科学基金资助项目(51865 010);江西省自然科学基金资助项目(20212BAB204007);江西省研究生创新资金资助项目(YC2020-S335,YC2021-S422)
- DOI：10.7652/xjtuxb202208002
  中图分类号： TH212;TH213.3
- 网络首发：2022-08-10，
  
  纸质出版：2022
- 稿件说明：
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张龙, 张号, 周建民, 等. 采用显式动力学的轴承性能退化评估指标构建[J]. 西安交通大学学报, 2022,56(8):11-21.

ZHANG Long, ZHANG Hao, ZHOU Jianmin, et al. Construction of Bearing Performance Degradation Assessment Indicator Using Explicit Dynamics[J]. 2022, 56(8): 11-21.
张龙, 张号, 周建民, 等. 采用显式动力学的轴承性能退化评估指标构建[J]. 西安交通大学学报, 2022,56(8):11-21. DOI： 10.7652/xjtuxb202208002.

ZHANG Long, ZHANG Hao, ZHOU Jianmin, et al. Construction of Bearing Performance Degradation Assessment Indicator Using Explicit Dynamics[J]. 2022, 56(8): 11-21. DOI： 10.7652/xjtuxb202208002.

摘要

针对非平稳工况下常规时域统计特征对滚动轴承早期微弱故障不敏感的问题

提出一种对早期微弱故障敏感且能表征性能退化趋势的性能退化评估指标

即特征相似比(FSI)。首先

对4种变工况轴承振动信号进行测试

分析统计特征对径向载荷、转速波动和故障程度的敏感性; 然后

选择对工况不敏感但对故障程度敏感的特征作为最佳特征; 最后

通过计算相邻两样本最佳特征的相似比构建FSI指标。采用所构建的FSI指标对轴承仿真故障数据和全寿命疲劳试验数据进行试验验证

结果表明:轴承发生早期故障时

最佳特征幅值增加9.4%

高于均方根值的4.7%、绝对均值的5.2%; 全寿命轴承出现微弱故障时

FSI幅值增加21.8%

高于均方根值的6.5%

且FSI准确表征了轴承性能退化趋势。

Abstract

In view of the problem that the conventional time-domain statistical features are not sensitive to the early weak fault signals of rolling bearings under non-stationary conditions

this paper proposes a performance degradation assessment indicator

namely the feature similarity indicator(FSI)that is sensitive to the early weak fault signals and characterizes the performance degradation trend. Firstly

four vibration signals of the bearings under variable conditions are tested to analyze the sensitivity of statistical features to radial load

speed fluctuation and failure degree; then

the features that are not sensitive to the conditions but sensitive to the failure degree are selected as the best features; finally

the FSI is constructed by calculating the similarity ratio between the best features of two adjacent samples. The FSI is used to verify the bearing simulation failure data and life cycle test data

and the results show that:when the bearing shows early fault

the magnitude of the best feature increases by 9.4%

which is higher than 4.7% of the root mean square value and 5.2% of the absolute mean value; when the full-life-cycle bearing shows early weak fault

the magnitude of FSI increases by 21.8%

which is higher than 6.5% of the root mean square value

and FSI accurately characterizes the trend of bearing performance degradation.

关键词

Keywords

references

LI Song, CHEN Zhiyong, LIU Qiaobin, et al. Modeling and analysis of performance degradation data for reliability assessment: a review [J]. IEEE Access, 2020, 8: 74648-74678.

NIU Guangxing, WANG Xuan, GOLDA M, et al. An optimized adaptive PReLU-DBN for rolling element bearing fault diagnosis [J]. Neurocomputing, 2021, 445: 26-34.

朱文昌, 罗梦婷, 倪广县, 等. 随机矩阵理论和主成分分析融合的滚动轴承性能退化评估方法 [J]. 西安交通大学学报, 2021, 55(2): 55-63.

ZHU Wenchang, LUO Mengting, NI Guangxian, et al. An evaluation method for rolling bearing performance degradation combining random matrix theory and principal component analysis [J]. Journal of Xi'an Jiaotong University, 2021, 55(2): 55-63.

LEI Yaguo, LI Naipeng, GUO Liang, et al. Machinery health prognostics: a systematic review from data acquisition to RUL prediction [J]. Mechanical Systems and Signal Processing, 2018, 104: 799-834.

LI Yazhou, DAI Wei, ZHANG Weifang. Bearing fault feature selection method based on weighted multidimensional feature fusion [J]. IEEE Access, 2020, 8: 19008-19025.

刘蕴哲, 胡金海, 任立通, 等. 基于特征选择与概率神经网络的轴承故障诊断研究 [J]. 机械传动, 2016, 40(10): 48-53.

LIU Yunzhe, HU Jinhai, REN Litong, et al. Study on the bearing fault diagnosis based on feature selection and probabilistic neural network [J]. Journal of Mechanical Transmission, 2016, 40(10): 48-53.

戴豪民, 许爱强, 李文峰, 等. 基于WMRMR的滚动轴承混合域特征选择方法 [J]. 振动与冲击, 2015, 34(19): 57-61.

DAI Haomin, XU Aiqiang, LI Wenfeng, et al. Fault diagnosis of rolling bearings in mixed domain based on WMRMR [J]. Journal of Vibration and Shock, 2015, 34(19): 57-61.

TUBISHAT M, ALSWAITTI M, MIRJALILI S, et al. Dynamic butterfly optimization algorithm for feature selection [J]. IEEE Access, 2020, 8: 194303-194314.

张龙, 吴荣真. 滚动轴承性能退化评估的自适应频带熵能比指标 [J]. 振动与冲击, 2021, 40(2): 63-71, 110.

ZHANG Long, WU Rongzhen. Performance degradation assessment of rolling bearings based on the entropy energy ratio of adaptive frequency band [J]. Journal of Vibration and Shock, 2021, 40(2): 63-71, 110.

黄海凤, 高宏力, 李丹, 等. 滚动轴承早期性能退化评估技术研究 [J]. 机械科学与技术, 2017, 36(11): 1771-1777.

HUANG Haifeng, GAO Hongli, LI Dan, et al. Study on evaluation of incipient performance degradation of rolling bearings [J]. Mechanical Science and Technology for Aerospace Engineering, 2017, 36(11): 1771-1777.

ZHU Keheng, JIANG Xiaohui, CHEN Liang, et al. Performance degradation assessment of rolling element bearings using improved fuzzy entropy [J]. Measurement Science Review, 2017, 17(5): 219-225.

李华, 伍星, 刘韬, 等. 基于信息熵优化变分模态分解的滚动轴承故障特征提取 [J]. 振动与冲击, 2018, 37(23): 219-225.

LI Hua, WU Xing, LIU Tao, et al. Bearing fault feature extraction based on VMD optimized with information entropy [J]. Journal of Vibration and Shock, 2018, 37(23): 219-225.

LIU Xiaoyang, HUANG Haizhou, XIANG Jiawei. A personalized diagnosis method to detect faults in a bearing based on acceleration sensors and an FEM simulation driving support vector machine [J]. Sensors, 2020, 20(2): 420.

MOTAHARI-NEZHAD M, JAFARI S M. Bearing remaining useful life prediction under starved lubricating condition using time domain acoustic emission signal processing [J]. Expert Systems with Applications, 2021, 168: 114391.

HALLQUIST J O. LS-DYNA theory manual [J]. Livermore Software Technology Corporation, 2006, 3: 25-31.

SINGH S, HOWARD C Q, HANSEN C H, et al. Analytical validation of an explicit finite element model of a rolling element bearing with a localised line spall [J]. Journal of Sound and Vibration, 2018, 416: 94-110.

SAFIAN A, ZHANG Hongsheng, LIANG Xihui, et al. Dynamic simulation of a cylindrical roller bearing with a local defect by combining finite element and lumped parameter models [J]. Measurement Science and Technology, 2021, 32(12): 125111.

LI Hongru, WANG Yukui, WANG Bing, et al. The application of a general mathematical morphological particle as a novel indicator for the performance degradation assessment of a bearing [J]. Mechanical Systems and Signal Processing, 2017, 82: 490-502.

ZHU Keheng. Performance degradation assessment of rolling element bearings based on hierarchical entropy and general distance [J]. Journal of Vibration and Control, 2018, 24(14): 3194-3205.

YAN Tongtong, WANG Dong, KONG Jinzhen, et al. Definition of signal-to-noise ratio of health indicators and its analytic optimization for machine performance degradation assessment [J]. IEEE Transactions on Instrumentation and Measurement, 2021, 70: 1-16.

YANG Chuangyan, MA Jun, WANG Xiaodong, et al. A novel based-performance degradation indicator RUL prediction model and its application in rolling bearing [J]. ISA Transactions, 2022, 121: 349-364.

LEI Y, HAN T, WANG B, et al. Interpretation of XJTU-SY rolling bearing accelerated life tested dataset [J]. Chin J Mech Eng, 2019, 55: 1-6.

张龙, 吴荣真, 周建民, 等. 滚动轴承性能退化的时序多元状态估计方法 [J]. 振动测试与诊断, 2021, 41(6): 1096-1104.

ZHANG Long, WU Rongzhen, ZHOU Jianmin, et al. Performance degradation assessment of rolling bearing based on AR model and multivariate state estimation technique [J]. Journal of Vibration, Measurement Diagnosis, 2021, 41(6): 1096-1104.

HU Mantang, WANG Guofeng, MA Kaile, et al. Bearing performance degradation assessment based on optimized EWT and CNN [J]. Measurement, 2021, 172: 108868.

杨创艳, 马军, 王晓东, 等. 特征融合与灰色回归的滚动轴承性能退化评估 [J]. 电子学报, 2022, 50(1): 106-115.

YANG Chuangyan, MA Jun, WANG Xiaodong, et al. Feature fusion and grey regression for performance degradation assessment of rolling bearings [J]. Acta Electronica Sinica, 2022, 50(1): 106-115.

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