基于数学形态学分段分形维数的电机滚动轴承故障模式识别

王冰;李洪儒;许葆华

振动与冲击 ›› 2013, Vol. 32 ›› Issue (19) : 28-31.

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PDF(1513 KB)
振动与冲击 ›› 2013, Vol. 32 ›› Issue (19) : 28-31.
论文

基于数学形态学分段分形维数的电机滚动轴承故障模式识别

  • 王冰,李洪儒,许葆华
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Fault pattern recognition for electromotor rolling bearing based on mathematical morphology sectionalized fractal dimension

  • WANG Bing, LI Hong-ru, XU Bao-hua
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摘要

电机轴承是旋转机械中应用最广且最易损坏的机械零件之一,分形维数可以有效地描述滚动轴承振动信号的复杂性和不规则性。基于数学形态学的分形维数具有计算速度快,估计准确的特点,可以正确地区分滚动轴承系统的状态和判断轴承系统的故障行为。本文阐述了基于数学形态学的分形维数计算方法,,针对扁平结构元素长度的选取缺乏指导性的问题,提出一种基于数学形态学的分段分形维数计算方法,运用该方法对电机轴承实测信号进行分析,结果表明,该方法在一定程度上提高了分形维数计算的科学性和精确性,在电机轴承故障模式识别领域是行之有效的。

Abstract

Roller bearing is one of the most widely used and easily damaged elements in rotary. Fractal dimensions could describe the complexity and irregular in bearing vibrating signal. Fractal dimension based on mathematical morphology has advantages in calculating speed and accuracy, which could distinguish roller bearing status and estimate its faulted behavior accurately. The calculating method of mathematical morphological fractal dimension was stated in the paper, In calculating procedure of fractal dimension, it always lacks guidance for choosing the length of flat structuring element. In allusion to this issue, a new calculating method named sectionalized fractal dimension was proposed in this paper. Applying this method into evaluating and analysis of vibration signal for different faulted roller bearing, it is obvious that the scientificity and preciseness for fractal dimension is improved and it is effective in roller bearing fault pattern recognition.



关键词

分段分形维数 / 数学形态学 / 电机轴承 / 故障模式识别

Key words

sectionalized fractal dimension / mathematical morphology / roller bearing / fault pattern recognition

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王冰;李洪儒;许葆华. 基于数学形态学分段分形维数的电机滚动轴承故障模式识别[J]. 振动与冲击, 2013, 32(19): 28-31
WANG Bing;LI Hong-ru;XU Bao-hua. Fault pattern recognition for electromotor rolling bearing based on mathematical morphology sectionalized fractal dimension[J]. Journal of Vibration and Shock, 2013, 32(19): 28-31

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