Bearing Fault Diagnosis Combined with Weighted Refined Composite Multiscale Dispersion Entropy and Improved Bayesian Networks

仝兆景; 孟令强; 唐晋豪; 吴鹏

doi:10.13433/j.cnki.1003-8728.20230276

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Bearing Fault Diagnosis Combined with Weighted Refined Composite Multiscale Dispersion Entropy and Improved Bayesian Networks

更新时间：2026-04-23

- Bearing Fault Diagnosis Combined with Weighted Refined Composite Multiscale Dispersion Entropy and Improved Bayesian Networks
- Mechanical Science and Technology for Aerospace Engineering Vol. 44, Issue 7, Pages: 1151-1158(2025)
- 作者机构：
  
  河南理工大学机构电气学院,河南,焦作,454003
- 作者简介：
- 基金信息：
- DOI：10.13433/j.cnki.1003-8728.20230276
  CLC：
- Published：2025
- 稿件说明：
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仝兆景, 孟令强, 唐晋豪, et al. Bearing Fault Diagnosis Combined with Weighted Refined Composite Multiscale Dispersion Entropy and Improved Bayesian Networks[J]. Mechanical Science and Technology for Aerospace Engineering, 2025, 44(7): 1151-1158.
DOI：

仝兆景, 孟令强, 唐晋豪, et al. Bearing Fault Diagnosis Combined with Weighted Refined Composite Multiscale Dispersion Entropy and Improved Bayesian Networks[J]. Mechanical Science and Technology for Aerospace Engineering, 2025, 44(7): 1151-1158. DOI： 10.13433/j.cnki.1003-8728.20230276.

摘要

针对多尺度散布熵方法无法准确估计信号复杂性的问题，为更精确地提取轴承振动信号的故障特征，将加权精细复合多尺度散布熵(Weighted refined composite multiscale dispersion entropy，wRCMDE)引入到轴承故障特征提取中。在此基础上，提出了一种基于wRCMDE与改进贝叶斯网络相结合的滚动轴承故障诊断方法。通过计算不同故障振动信号的wRCMDE，并选取合适尺度下的多个wRCMDE值作为特征向量形成特征样本，输入到改进萤火虫算法优化的贝叶斯网络中进行故障分类识别。通过实验数据分析，将所提方法与基于多尺度散布熵和精细复合多尺度散布熵的故障特征提取方法进行对比

结果表明，该方法能够更加准确地识别滚动轴承的故障类型，且识别率更高。

Abstract

In order to extract the fault features of bearing vibration signals more accurately

weighted fine composite multi-scale spread entropy (wRCMDE) was introduced into bearing fault feature extraction. On this basis

a rolling bearing fault diagnosis method based on wRCMDE and improved Bayesian network was proposed. By calculating wRCMDE of different fault vibration signals and selecting multiple wRCMDE values at appropriate scale as feature vectors

feature samples were formed and input into the Bayesian network optimized by the improved firefly algorithm for fault classification and recognition. Through the analysis of experimental data

the proposed method is compared with the fault feature extraction method based on multiscale dispersal entropy and refined composite multiscale dispersal entropy. Experimental results show that this method can identify the fault types of rolling bearings more accurately

and the recognition rate is higher.

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