基于多重分形去趋势波动分析的齿轮箱故障特征提取方法

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振动与冲击 ›› 2013, Vol. 32 ›› Issue (2) : 97-101.

论文

基于多重分形去趋势波动分析的齿轮箱故障特征提取方法

林近山 1，2 ，陈前 1

作者信息 +

Fault Feature Extraction of Gearboxes Based on Multifractal Detrended Fluctuation Analysis

LIN Jin-shan 1,2 CHEN Qian 1

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文章历史 +

摘要

齿轮箱故障信号通常是具有多标度行为的非平稳信号，去趋势波动分析(Detrended Fluctuation Analysis, DFA)不能准确揭示隐藏在这类信号中的动力学行为。多重分形去趋势波动分析(Multifractal Detrended Fluctuation Analysis, MF-DFA)是DFA方法的拓展，能够有效地揭示隐藏在多标度非平稳信号中的动力学行为。利用MF-DFA计算齿轮箱故障信号的多重分形奇异谱，而多重分形奇异谱的宽度、最大奇异指数、最小奇异指数和极值点对应的奇异指数都具有明确的物理意义，能够表征齿轮箱故障信号的内在动力学机制，适合作为齿轮箱振动信号的故障特征。提出一种基于MF-DFA的齿轮箱故障特征提取方法，将该方法用于包含正常、轻度磨损、中度磨损和断齿故障齿轮箱的故障诊断，并与DFA方法的结果进行了对比。结果表明，提出的方法对齿轮箱故障状态的变化非常敏感，能够完全分离相近的故障模式，有效地克服了传统DFA方法存在的缺陷，为齿轮箱的故障特征提取提供了一种新方法。

Abstract

Gearbox fault data are usually characterized by nonstationarity and multiple scaling behaviors, whose underlying dynamical mechanism detrended fluctuation analysis (DFA) often fails to uncover. Multifractal DFA (MF-DFA) is an extension of DFA and able to effectively reveal the underlying dynamical mechanism hiding in nonstationary data with multiple scaling behaviors. To start with, MF-DFA was used to computer the multifractal singularity spectrum of gearbox fault data. Next, the four characteristic parameters, including the multifractal spectrum width, the maximum singularity exponent, the minimum singularity exponent and the singularity exponent corresponding to the extremum of the multifractal spectrum, bear clear physical meaning, express the underlying dynamical mechanism of gearbox fault data and can be employed as fault features of gearbox fault data. Consequently, a novel method for feature extraction of gearbox fault data was proposed based on MF-DFA. Besides, the proposed method together with DFA was utilized to separate the normal, the slight-worn, the medium-worn and the broken-tooth vibration data from a four-speed motorcycle gearbox. The results show that the proposed method, overcoming the deficiencies of DFA, is sensitive to small changes of gearbox fault conditions, can totally separate the similar and close fault patters and seems to be a feasible method for feature extraction of gearbox fault data.

导出引用

林近山;;陈前 . 基于多重分形去趋势波动分析的齿轮箱故障特征提取方法[J]. 振动与冲击, 2013, 32(2): 97-101

LIN Jin-shan;CHEN Qian . Fault Feature Extraction of Gearboxes Based on Multifractal Detrended Fluctuation Analysis[J]. Journal of Vibration and Shock, 2013, 32(2): 97-101