A time domain approach to diagnose gearbox fault based on measured vibration signals
A time domain approach to diagnose gearbox fault based on measured vibration signals
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DOI：
10.1016/j.jsv.2013.11.033
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发表时间：
2014-03-31
影响因子：
4.7
通讯作者：
Dhupia, Jaspreet Singh
中科院分区：
文献类型：
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作者：
Hong, Liu;Dhupia, Jaspreet Singh
用来处理振动测量的频谱分析技术已被广泛研究，以表征变速箱的状态。然而，在实际中，由于工业环境中经常出现的有限的频率分辨率和机器运行速度的微小波动，由局部齿轮故障引起的调制边带往往很难从模糊/模糊的测量振动谱中准确地提取出来。为了解决这一问题，本文提出了一种新的用于齿轮故障监测的时域诊断算法，该算法提高了从测量的振动信号中提取故障的能力。这种新的时域故障检测方法结合了快速动态时间规整(Fast DTW)和相关峰度(CK)技术来表征齿轮的局部故障，并识别出相应的故障齿轮及其位置。该方法利用齿轮箱在假定健康状态下的振动特性，利用与名义齿轮啮合谐波频率相同的估计参考信号，利用快速DTW提取故障齿轮齿引起的周期性脉冲激励。这种技术在实际分析中是有益的，以便在数据经常受到过程/测量噪声和运行速度的微小波动污染的情况下突出边带模式，即使在其他假定的稳态条件下也是如此。然后，使用CK技术对提取的信号进行重新采样以进行后续的诊断分析。它利用齿轮故障的周期性，用于识别变速箱齿轮局部故障的位置。基于模拟的齿轮振动信号，基于快速DTW和CK的方法被证明对定轴和周转变速箱的状态监测都是有效的。最后，用行星变速箱试验台的实验信号验证了该方法在齿轮故障检测中的有效性。对于行星轮系的故障检测，引入了一个窗函数来描述行星相对于固定传感器的运动，该窗函数是由实验确定的，后来被用于快速DTW算法中使用的参考信号的估计。(C)2013爱思唯尔有限公司。保留所有权利。
Spectral analysis techniques to process vibration measurements have been widely studied to characterize the state of gearboxes. However, in practice, the modulated sidebands resulting from the local gear fault are often difficult to extract accurately from an ambiguous/blurred measured vibration spectrum due to the limited frequency resolution and small fluctuations in the operating speed of the machine that often occurs in an industrial environment. To address this issue, a new time-domain diagnostic algorithm is developed and presented herein for monitoring of gear faults, which shows an improved fault extraction capability from such measured vibration signals. This new time-domain fault detection method combines the fast dynamic time warping (Fast DTW) as well as the correlated kurtosis (CK) techniques to characterize the local gear fault, and identify the corresponding faulty gear and its position. Fast DTW is employed to extract the periodic impulse excitations caused from the faulty gear tooth using an estimated reference signal that has the same frequency as the nominal gear mesh harmonic and is built using vibration characteristics of the gearbox operation under presumed healthy conditions. This technique is beneficial in practical analysis to highlight sideband patterns in situations where data is often contaminated by process/measurement noises and small fluctuations in operating speeds that occur even at otherwise presumed steady-state conditions. The extracted signal is then resampled for subsequent diagnostic analysis using CK technique. CK takes advantages of the periodicity of the geared faults; it is used to identify the position of the local gear fault iii the gearbox. Based on simulated gear vibration signals, the Fast DTW and CK based approach is shown to be useful for condition monitoring in both fixed axis as well as epicyclic gearboxes. Finally the effectiveness of the proposed method in fault detection of gears is validated using experimental signals from a planetary gearbox test rig. For fault detection in planetary gear-sets, a window function is introduced to account for the planet motion with respect to the fixed sensor, which is experimentally determined and is later employed for the estimation of reference signal used in Fast DTW algorithm. (C) 2013 Elsevier Ltd. All rights reserved.