• CN:11-2187/TH
  • ISSN:0577-6686

机械工程学报 ›› 2016, Vol. 52 ›› Issue (15): 59-64.doi: 10.3901/JME.2016.15.059

• 机械动力学 • 上一篇    下一篇

流形模糊C均值方法及其在滚动轴承性能退化评估中的应用*

王奉涛1, 陈旭涛1, 闫达文2, 李宏坤1, 王雷1, 朱泓1   

  1. 1. 大连理工大学振动工程研究所 大连 116023
    2. 大连理工大学数学科学学院 大连 116023
  • 出版日期:2016-08-05 发布日期:2016-08-05
  • 作者简介:

    王奉涛(通信作者),男,1974年出生,博士,副教授。主要研究方向为设备故障诊断与寿命预测、振动与噪声。

    E-mail:wangft@dlut.edu.cn

  • 基金资助:
    * 国家自然科学基金(51375067)、航空科学基金(20132163010)和中央高校基本科研业务费专项资金资助项目; 20150923收到初稿,20160423收到修改稿;

Fuzzy C-means Using Manifold Learning and Its Application to Rolling Bearing Performance Degradation Assessment

WANG Fengtao1, CHEN Xutao1, YAN Dawen2, LI Hongkun1, WANG Lei1, ZHU Hong1   

  1. 1. Institute of Vibration Engineering, Dalian University of Technology, Dalian 116023
    2. School of Mathematical Sciences, Dalian University of Technology, Dalian 116023
  • Online:2016-08-05 Published:2016-08-05

摘要:

滚动轴承全寿命周期性能退化监测是设备主动维修技术重要的组成部分,对损伤状态进行有效评估可以实现设备接近零停机运行,发挥机器的最大生产力。为有效描绘滚动轴承性能退化趋势,提出一种基于流形学习的模糊C均值(Fuzzy C-means algorithm,FCM)方法。首先提取监测信号的时域、频域特征及小波包时频域特征组成高维特征集,然后按确定的本征维数提取高维特征集的低维流形特征,进而建立基于局部线性嵌入流行学习(Locally linear embedding,LLE)的模糊C均值模型评估轴承当前运行状态。通过IMS滚动轴承全寿命试验,验证了该方法能够有效描绘滚动轴承性能退化阶段,为预知维修提供了重要信息。

关键词: 滚动轴承, 模糊C均值, 性能退化, LLE流形

Abstract:

:Entire life cycle degradation monitoring of rolling bearing is an important part of equipment initiative maintenance technology. Assessing the damage state effectively can realize near-zero downtime for equipment and exert its maximum productivity. In order to depict rolling bearing degradation trends effectively, the fuzzy C-means Algorithm (FCM) based on manifold learning is proposed. First of all, the time domain features, frequency domain features and wavelet packet time-frequency domain characteristics extracted from monitoring signals are used to constitute high-dimensional feature set. After then, the low-dimensional manifold features of the high-dimensional feature set are extracted according to the certain intrinsic dimension. In this sense, the FCM model based on locally linear embedding (LLE) manifold learning is built to evaluate current operating status of the rolling bearing. Finally, entire life cycle experiment of IMS rolling bearing is used to evaluate the efficiency of the proposed method for describing performance degradation stage of the rolling bearing.

Key words: fuzzy C-means, performance degradation, rolling bearing, LLE manifold learning