Feature Fusion and Grey Regression for Performance Degradation Assessment of Rolling Bearings

YANG Chuang-yan; MA Jun; WANG Xiao-dong; LUO Ting; LI Zhuo-rui

doi:10.12263/DZXB.20200826

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Feature Fusion and Grey Regression for Performance Degradation Assessment of Rolling Bearings

PAPERS | 更新时间：2025-12-08

- Feature Fusion and Grey Regression for Performance Degradation Assessment of Rolling Bearings
- ACTA ELECTRONICA SINICA Vol. 50, Issue 1, Pages: 106-115(2022)
- 作者机构：
  
  1.昆明理工大学信息工程与自动化学院，云南昆明 650500
  2.昆明理工大学云南省人工智能重点实验室，云南昆明 650500
- 作者简介：
- 基金信息：
- DOI：10.12263/DZXB.20200826
  CLC： TN911.7;
- Received：03 August 2020，
  
  Revised：2021-02-05，
  
  Published：25 January 2022
- 稿件说明：
移动端阅览
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(01):106-115.

YANG Chuang-yan,MA Jun,WANG Xiao-dong,et al.Feature Fusion and Grey Regression for Performance Degradation Assessment of Rolling Bearings[J].ACTA ELECTRONICA SINICA,2022,50(01):106-115.
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(01):106-115. DOI： 10.12263/DZXB.20200826.

YANG Chuang-yan,MA Jun,WANG Xiao-dong,et al.Feature Fusion and Grey Regression for Performance Degradation Assessment of Rolling Bearings[J].ACTA ELECTRONICA SINICA,2022,50(01):106-115. DOI： 10.12263/DZXB.20200826.

摘要

针对传统退化指标无法准确反映滚动轴承全寿命周期内退化状态的问题，提出一种特征融合与灰色回归的滚动轴承性能退化评估方法.该方法提取滚动轴承振动信号的高维退化特征，构建基于单调性、相关性和鲁棒性的综合评价准则，选择有效退化特征并构建敏感指标集；提出核独立成分分析（Kernel Independent Component Analysis，KICA）和马氏距离（Mahalanobis Distance，MD）相结合的方法，计算敏感退化指标KICAMD；融合灰色回归模型和

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原则，判定敏感退化指标KICAMD是否存在虚假波动并修复，获得轴承健康指标（Health Index，HI）；最后，基于HI时间序列的转折突变点，自适应确定初始故障时间和定量评估轴承退化状态.两组滚动轴承全寿命周期振动实验数据及对比分析表明，所提方法构建的性能退化指标能有效表征轴承全生命周期的运行状态.

Abstract

Aiming at the problem that the traditional degradation indicator cannot accurately reflect the degradation state of the rolling bearing in the whole life cycle

a method of performance degradation evaluation based on feature fusion and grey regression is proposed. The high-dimensional degradation features of vibration signal are extracted

and the comprehensive evaluation criteria based on monotonicity

correlation and robustness is constructed. A method combining kernel independent component analysis(KICA) and mahalanobis distance(MD) is proposed to calculate the sensitive degradation indicator KICAMD. Then

a novel based-the gray regression model and

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principle method is introduced to determine in advance whether the sensitivity degradation indicator KICAMD is false fluctuation ahead of time repair

and then the bearing degradation health indicator HI is obtained. Based on the abrupt transition point of HI time series

the start failure time is determined adaptively and the rolling bearing degradation state is quantitatively evaluated. The experiment and comparative analysis of two groups of rolling bearing life cycle vibration show that the constructed performance degradation index can effectively characterize the running state of the rolling bearing.

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references

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