A Diagnosis Method for Unbalance Fault of Motori-ed Spindles Using Symmetrical Polar Image and Clustering of Fu--y C-Means

doi:10.7652/xjtuxb201912008

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A Diagnosis Method for Unbalance Fault of Motori-ed Spindles Using Symmetrical Polar Image and Clustering of Fu--y C-Means

更新时间：2025-07-09

- A Diagnosis Method for Unbalance Fault of Motori-ed Spindles Using Symmetrical Polar Image and Clustering of Fu--y C-Means
- Vol. 53, Issue 12, Pages: 57-62+86(2019)
- 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：
- DOI：10.7652/xjtuxb201912008
  CLC： TH17;TH113.1
- Online First：10 December 2019，
  
  Published：2019
- 稿件说明：
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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：

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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references

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