Applying MMI-SSVP Method to Machine Tool Bearing Fault Feature Extraction

康怡; 刘韬; 施庆华; 王振亚

doi:10.13433/j.cnki.1003-8728.20230344

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Applying MMI-SSVP Method to Machine Tool Bearing Fault Feature Extraction

更新时间：2026-04-23

- Applying MMI-SSVP Method to Machine Tool Bearing Fault Feature Extraction
- Mechanical Science and Technology for Aerospace Engineering Vol. 44, Issue 10, Pages: 1774-1784(2025)
- 作者机构：
  
  1. 昆明理工大学机构机电工程学院,昆明,650500
  2. 云南省先进装备智能制造技术重点实验室,昆明,650500
  3. 中国机械总院集团云南分院有限公司,昆明,650031
- 作者简介：
- 基金信息：
- DOI：10.13433/j.cnki.1003-8728.20230344
  CLC：
- Published：2025
- 稿件说明：
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康怡, 刘韬, 施庆华, et al. Applying MMI-SSVP Method to Machine Tool Bearing Fault Feature Extraction[J]. Mechanical Science and Technology for Aerospace Engineering, 2025, 44(10): 1774-1784.
DOI：

康怡, 刘韬, 施庆华, et al. Applying MMI-SSVP Method to Machine Tool Bearing Fault Feature Extraction[J]. Mechanical Science and Technology for Aerospace Engineering, 2025, 44(10): 1774-1784. DOI： 10.13433/j.cnki.1003-8728.20230344.

摘要

基于奇异值分解(Singular value decomposition

SVD)重构能够有效分离和抑制监测信号中的随机噪声分量

但其性能受限于轨迹矩阵的构造、有效分量评估选择等因素的影响。针对该问题

提出了一种基于最小互信息(Min mutual information

MMI)自适应累加奇异值子对(Sum singular value pairs

SSVP)优化框架并应用于机床轴承故障信号的特征提取。首先

采用反对角平均法计算奇异值(Singular value

SV)和奇异值向量

利用SV对子信号能量的表征能力得到奇异值子对(Singular value pairs

SVP); 然后

基于MMI指标自适应获取最佳重构分量

避免了过降噪或欠降噪; 同时

利用MMI和奇异值比(Singular value ratio

SVR)指标联合确定Hankel矩阵的最优分解维数。最后利用主轴故障轴承数据以及工业现场某加工中心进给系统轴承故障数据验证了MMI-SSVP方法的有效性。

Abstract

The reconstruction based on the singular value decomposition can effectively separate and suppress random noise components in monitoring signals

but its performance is limited by the construction of trajectory matrixes and effective component evaluation and selection. To solve this problem

an adaptive sum singular value pair (SSVP) optimization framework based on the min mutual information (MMI) is proposed and applied to the feature extraction of machine tool bearing fault signals. Firstly

singular value and singular value vector are calculated with the anti-angular average method

and singular value pairs (SVP) are obtained with the characterization ability of the SVP of sub-signal energy. Then

the optimal reconstructed components are obtained adaptively based on the MMI index

avoiding over-noise reduction or under-noise reduction. Meanwhile

the singular value ratio indexes of MMI are combined to determine the number of optimal decomposition dimensions of the Hankel matrix. Finally

the validity of the MMI-SSVP method is verified with the data of the faulty bearings of a spindle and the feeding system of a machining center in an industrial site respectively.

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