Study on fractal features extracted from acoustic emission signals and fault diagnosis of journal bearing

LU Xuxiang; LIU Yujia; TANG Shengkun; SU Yiming; WU Jiateng; LI Luping

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Study on fractal features extracted from acoustic emission signals and fault diagnosis of journal bearing

更新时间：2025-11-18

- Study on fractal features extracted from acoustic emission signals and fault diagnosis of journal bearing
- Vol. 35, Issue 4, Pages: 64-68(2015)
- 作者机构：
  
  长沙理工大学
- 作者简介：
- 基金信息：
- DOI：
  CLC： TH165.3;TH133.31
- Published：2015
- 稿件说明：
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[1]卢绪祥,刘雨佳,唐晟锟,苏一鸣,吴家腾,李录平.滑动轴承声发射信号分形特征提取及故障诊断研究[J].湖南电力,2015,35(04):64-68.

LU Xuxiang, LIU Yujia, TANG Shengkun, et al. Study on fractal features extracted from acoustic emission signals and fault diagnosis of journal bearing[J]. 2015, 35(4): 64-68.
[1]卢绪祥,刘雨佳,唐晟锟,苏一鸣,吴家腾,李录平.滑动轴承声发射信号分形特征提取及故障诊断研究[J].湖南电力,2015,35(04):64-68. DOI：

LU Xuxiang, LIU Yujia, TANG Shengkun, et al. Study on fractal features extracted from acoustic emission signals and fault diagnosis of journal bearing[J]. 2015, 35(4): 64-68. DOI：

摘要

针对滑动轴承声发射信号具有随机性、瞬态性、时变性、多态性和易受干扰的特点

为有效地提取早期故障特征、提高在线监测效率

采用关联维数作为判断滑动轴承润滑故障的特征值。首先基于310 MW汽轮发电机组滑动轴承现场试验获得的声发射信号

分析滑动轴承声发射信号的分形特性并将其与传统的声发射特征值进行对比;然后利用关联维数对滑动轴承润滑状态进行定量分析;最后针对经典的G-P关联维数算法计算量较大的问题

对算法进行改进

并通过计算时间的对比证明改进后的算法计算效率更高。结果表明:关联维数不仅与滑动轴承润滑状态具有正相关性和一一对应性

而且更能敏感地表征滑动轴承的早期故障;采用改进后的关联维数算法能有效地提高监测效率

更适用于现场实时监测以保证机组安全稳定运行。

Abstract

For the acoustic emission signals of journal bearings are with of the characteristics of random

transient

timevarying

polymorphism and susceptible to interference

the correlation dimension has been taken as characteristic value to diagnose the lubricating fault in order to extract early failure characteristics effectively and improve the efficiency of online monitoring. Firstly

the acoustic emission signals of 310 MW turbo-generator set by mean of field test is analyzed to obtain its fractal characteristics

and then compared with traditional acoustic emission characteristic value. Then the lubricating condition of the journal bearing is quantitatively analyzed by means of correlation dimension. Finally

the algorithm of classic G-P correlation dimension is improved due to larger computational complexity. The proved algorithm is of more efficient than the classic by comparing the calculation time between them. The results show that correlation dimension not only have positive correlation and correspondence with the lubricating condition of the journal bearing

but also is more sensitive to characterization of journal bearing early faults. The improved correlation dimension algorithm can effectively improve the monitoring efficiency

more suitable for the real-time monitoring field to ensure the safe and stable operation of the unit.

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