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基于数学形态学的分形维数计算及在轴承故障诊断中的应用 被引量:30

Mathematic morphology-based fractal dimension calculation and its application in fault diagnosis of roller bearings
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摘要 滚动轴承故障信号是一种典型的非线性信号,分形几何为描述轴承故障信号的特性提供了一个有力的分析工具。基于数学形态学的分形维数是在Minkowski-Boulingand维数基础上拓展的一种采用形态学操作计算分形维数的新方法。较详细的阐述了基于数学形态学的分维数计算方法,对比分析了与传统盒维数方法的区别与联系,并对实际的滚动轴承正常、滚动体故障、内圈故障和外圈故障信号进行了分析,结果表明,基于数学形态学的分维数计算方法具有计算速度快,估计准确稳定的特点,为准确判断滚动轴承故障状态提供了一种快速有效的新方法。 The vibration signal generated from defected roller bearings is a typical nonlinear one.The fractal theory provides an effective approach to analysis characteristic of a roller bearing fault signal.As an extension of the traditional Minkowski-Boulingand fractal dimension,mathematical morphology-based fractal dimension is calculated via the morphological operation.This new fractal estimation method was studied in detail.A comparison between the new fractal dimension and the traditional box dimension was made,the new method was employed to analyze the real vibration signals acquired from four different states of roller bearing,i.e,normal,roller element defect,inner race defect and outer race defect.The results revealed that the mathematical morphology-based fractal dimension has higher accuracy and less calculation cost,and it is an effective tool for fault diagnosis of roller bearings.
出处 《振动与冲击》 EI CSCD 北大核心 2010年第5期191-194,共4页 Journal of Vibration and Shock
基金 国家自然科学基金(50705097) 河北省自然科学基金(E2007001048)资助
关键词 分形 数学形态学 滚动轴承 故障诊断 特征提取 fractal dimension mathematical morphology roller bearing fault diagnosis feature extraction
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参考文献13

  • 1Logan D, Mathew J. Using the correlation dimension for vibration fault diagnosis of rolling element bearings-Ⅰ. Basic concepts[ J ]. Mechanical Systems and Signal Processing, 1996. 10(3) :241 -250.
  • 2Logan D B, Mathew J. Using the correlation dimension for vibration fault diagnosis of rolling element bearings-Ⅱ. Selection of experimental parameters [ J ]. Mechanical Systems and Signal Processing, 1996. 10(3) :251 -264.
  • 3吕志民,徐金梧,翟绪圣.分形维数及其在滚动轴承故障诊断中的应用[J].机械工程学报,1999,35(2):88-91. 被引量:70
  • 4訾艳阳,胥永刚,何正嘉.离散振动信号分形盒维数的改进算法和应用[J].机械科学与技术,2001,20(3):373-375. 被引量:23
  • 5Chaudhuri B B, Sarkar N. Texture segmentation using fractal dimension[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1995. 17( 1 ) :72 -77.
  • 6Maragos P, Sun F K. Measuring the fractal dimension of signals: morphological covers and iterative optimization [ J ]. IEEE Transactions on Signal Processing, 1993. 41 ( 1 ) : 108 -121.
  • 7Xia Y, Feng D, Zhao R. Morphology-based multifractal estimation for texture segmentation [J]. IEEE Transactions on Image Processing, 2006. 15 (3) : 614 - 623.
  • 8Maragos P, Potamianos A. Fractal dimensions of speech sounds: Computation and application to automatic speech recognition[J]. Journal of the Acoustical Society of America, 1999. 105(3) :1925 - 1932.
  • 9Accardo A, Affinito M, Carrozzi M, et al. Use of the fractal dimension for the analysis of electroencephalographic time series [ J ]. Biological Cybernetics, 1997. 77 ( 5 ) : 339 - 350.
  • 10Maragos P, Schafer R W. Morphological fihers-part i: their set-theoretic anal ysis and relations to linear shift-invariant filters [ J ]. IEEE Transactions on Acoustics, Speech, and Signal Processing, 1987. ASSP -35(8) :1153 - 1169.

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