Rolling Bearing Fault Diagnosis Approach Based on Case-Based Reasoning

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Rolling Bearing Fault Diagnosis Approach Based on Case-Based Reasoning

更新时间：2025-07-09

- Rolling Bearing Fault Diagnosis Approach Based on Case-Based Reasoning
- Vol. 45, Issue 11, Pages: 79-84(2011)
- 作者机构：
  
  西安交通大学机械工程学院,西安,710049
- 作者简介：
- 基金信息：
- DOI：
  CLC： TH17
- Online First：10 November 2011，
  
  Published：2011
- 稿件说明：
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Rolling Bearing Fault Diagnosis Approach Based on Case-Based Reasoning[J]. 2011, 45(11): 79-84.
DOI：

Rolling Bearing Fault Diagnosis Approach Based on Case-Based Reasoning[J]. 2011, 45(11): 79-84. DOI：

摘要

针对滚动轴承的故障诊断问题

提出了一种采用案例推理(CBR)的诊断方法.为了解决检索相似案例时案例属性多、人工确定关键属性及其权重困难的问题

提出了一种Filter/Wrapper复合型特征选择算法

用邻域粗糙集算法粗选属性

用遗传算法进一步精选属性和优化权重

并有效地解决了邻域粗糙集算法中需要人工确定邻域大小的问题.以滚动轴承运行时的振动信号为基本信息

建立了滚动轴承案例库

从案例库中检索与问题案例相似的历史案例

并根据这些历史案例来判定问题案例的故障类别.试验结果表明

故障诊断的正确率达到100%

故障位置诊断的正确率达到93.3%

且算法具有较好的稳定性.

Abstract

The case-based reasoning approach is introduced into rolling bearing fault diagnosis. To solve the complexity of feature selection and weights optimization

a Filter/Wrapper integrated feature selection algorithm is proposed. Neighborhood rough set algorithm is applied to select essential features from the feature candidate set

then genetic algorithm is applied to refine the essential feature subset.This method solves the problem of determining the size of neighborhood manually in neighborhood rough set algorithm. Genetic algorithm is also used in feature weights optimization. With the runtime vibration signal of rolling bearing as the basic information

a rolling bearing fault case database is constructed.The historical cases similar to the problem case are recalled and chosen to decide the fault type. The database experiment shows the higher efficiency and accuracy for essential attributes and weights in fault diagnosis.

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references

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Feature Extraction for Incipient Fault Diagnosis of Rolling Bearings Based on Nonlinear Manifold Learning

Spectral Kurtosis of Multiwavelet for Fault Diagnosis of Rolling Bearing

Optimization of Stacking Auto-Encoder with Applications in Bearing Fault Diagnosis

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Fault Diagnosis Method for Rolling Bearing under Variable Working Conditions Using Improved Residual Neural Network

Related Author

栗茂林 1

王孙安 1

梁霖 1

王晓冬

何正嘉

訾艳阳

张西宁

Related Institution

State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University

College of Electrical Engineering and Information Engineering, Lanzhou University of Technology

Key Laboratory of Gansu Advanced Control for Industrial Processes

National Experimental Teaching Center of Electrical and Control Engineering, Lanzhou University of Technology

Key Laboratory of Gansu Advanced Control for Industrial Processes, Lanzhou University of Technology

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