一种对称极坐标图像模糊C均值聚类的电主轴失衡故障诊断方法

樊红卫 1; 邵偲洁 1; 张旭辉 1; 马宏伟 1; 曹现刚 1; 2; 景敏卿 3

doi:10.7652/xjtuxb201912008

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一种对称极坐标图像模糊C均值聚类的电主轴失衡故障诊断方法

更新时间：2025-07-09

- 一种对称极坐标图像模糊C均值聚类的电主轴失衡故障诊断方法
- A Diagnosis Method for Unbalance Fault of Motori-ed Spindles Using Symmetrical Polar Image and Clustering of Fu--y C-Means
- 西安交通大学学报 2019年53卷第12期页码：57-62+86
- 作者机构：
  
  1. 西安科技大学机械工程学院,西安,710054
  2. 陕西省矿山机电装备智能监测重点实验室,西安,710054
- 作者简介：
- 基金信息：
  
  : 2019-03-29。作者简介: 樊红卫(1984—),男,博士,讲师,硕士生导师。基金项目: 国家自然科学基金资助项目(51605380);陕西省重点研发计划资助项目(2019GY-093);陕西省博士后科研基金资助项目(2016BSHYDZZ08)
- DOI：10.7652/xjtuxb201912008
  中图分类号： TH17;TH113.1
- 网络首发：2019-12-10，
  
  纸质出版：2019
- 稿件说明：
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樊红卫 1, 邵偲洁 1, 张旭辉 1, 等. 一种对称极坐标图像模糊C均值聚类的电主轴失衡故障诊断方法[J]. 西安交通大学学报, 2019,53(12):57-62+86.

A Diagnosis Method for Unbalance Fault of Motori-ed Spindles Using Symmetrical Polar Image and Clustering of Fu--y C-Means[J]. 2019, 53(12): 57-62+86.
樊红卫 1, 邵偲洁 1, 张旭辉 1, 等. 一种对称极坐标图像模糊C均值聚类的电主轴失衡故障诊断方法[J]. 西安交通大学学报, 2019,53(12):57-62+86. DOI： 10.7652/xjtuxb201912008.

A Diagnosis Method for Unbalance Fault of Motori-ed Spindles Using Symmetrical Polar Image and Clustering of Fu--y C-Means[J]. 2019, 53(12): 57-62+86. DOI： 10.7652/xjtuxb201912008.

摘要

为解决电主轴转子不平衡故障的可视化智能识别问题

提出了一种对称极坐标图像和模糊C均值(FCM)聚类相结合的失衡故障诊断新方法。首先对转子时域振动信号进行经验模态分解降噪

按对称极坐标方法将其转化为二维雪花图像

通过灰度共生矩阵

提取雪花图像二维特征参数; 然后对已知样本信号的特征参数组建故障特征向量

标准化后作为FCM输入

得到分类矩阵和聚类中心; 最后计算待测样本和已知故障样本聚类中心贴进度

实现失衡故障识别和分类。在某电主轴系统平台上完成了1 800 r/min时转子3种不同失衡状态的诊断试验

在对45组小样本识别中该方法的分类准确率达到73%。

Abstract

A new unbalance diagnosis method based on the symmetrical polar coordinate image and clustering of fu--y C-means(FCM)is proposed for the unbalance fault of motori-ed spindle rotor. The empirical mode decomposition is used to reduce the noise of time-domain vibration signal of a rotor and then it is transformed into a two-dimensional snowflake image through using the symmetrical polar coordinate method. Two-dimensional feature parameters of the snowflake image are extracted by the gray level co-occurrence matrix. Vectors of fault features for the characteristic parameters of the known sample signals are constructed and standardi-ed as FCM input

then a classification matrix and clustering centers are obtained. The proximity between tested samples and the cluster centers of known fault samples are calculated to reali-e recognition and classification of the unbalance faults. Diagnostic tests of three different imbalance states of a rotor at 1 800 r/min are completed on the platform of a m

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