Dynamic modelling of spur gear pair and application of empirical mode decomposition-based statistical analysis for early detection of localized tooth defect
Dynamic modelling of spur gear pair and application of empirical mode decomposition-based statistical analysis for early detection of localized tooth defect
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DOI：
10.1016/j.jsv.2005.11.021
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发表时间：
2006-06
影响因子：
4.7
通讯作者：
Anand Parey;M. Badaoui;F. Guillet;N. Tandon
中科院分区：
文献类型：
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作者：
Anand Parey;M. Badaoui;F. Guillet;N. Tandon
齿轮是许多先进机器中最常见和最重要的机器部件之一。需要更好地了解振动信号，以便及早检测早期齿轮故障，以实现高可靠性。本文主要由两部分组成：第一部分，建立了包含局部齿缺陷的六自由度齿轮动力学模型。该模型由一个正齿轮副、两个轴、两个代表负载的惯量以及原动机和轴承组成。该模型结合了时变啮合刚度和阻尼、齿隙、齿轮误差和轮廓修改引起的激励的影响。第二部分包括模拟信号和实验信号的信号处理。经验模态分解 (EMD) 是一种在不离开时域的情况下分解信号的方法。该过程对于分析非平稳和非线性信号非常有用。 EMD 将信号分解为一些单独的、几乎单分量的信号，称为本征模态函数 (IMF)。已计算这些 IMF 的波峰因数和峰度。与原始信号相比，EMD 预处理的峰度和波峰因数可以及早检测点蚀。
Gears are one of the most common and important machine components in many advanced machines. An improved understanding of vibration signal is required for the early detection of incipient gear failure to achieve high reliability. This paper mainly consists of two parts: in the first part, a 6-degree-of-freedom gear dynamic model including localized tooth defect has been developed. The model consists of a spur gear pair, two shafts, two inertias representing load and prime mover and bearings. The model incorporates the effects of time-varying mesh stiffness and damping, backlash, excitation due to gear errors and profile modifications. The second part consists of signal processing of simulated and experimental signals. Empirical mode decomposition (EMD) is a method of breaking down a signal without leaving a time domain. The process is useful for analysing non-stationary and nonlinear signals. EMD decomposes a signal into some individual, nearly monocomponent signals, named as intrinsic mode function (IMF). Crest factor and kurtosis have been calculated of these IMFs. EMD pre-processed kurtosis and crest factor give early detection of pitting as compared to raw signal.