Construction of Bearing Performance Degradation Assessment Indicator Using Explicit Dynamics

ZHANG Long; ZHANG Hao; ZHOU Jianmin; PENG Xiaoming; WANG Xiaobo; QIAO Yu

doi:10.7652/xjtuxb202208002

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Construction of Bearing Performance Degradation Assessment Indicator Using Explicit Dynamics

更新时间：2025-07-09

- Construction of Bearing Performance Degradation Assessment Indicator Using Explicit Dynamics
- Vol. 56, Issue 8, Pages: 11-21(2022)
- 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：
- 基金信息：
- DOI：10.7652/xjtuxb202208002
  CLC： TH212;TH213.3
- Online First：10 August 2022，
  
  Published：2022
- 稿件说明：
移动端阅览
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：

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

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