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Optimal Filtering Of Gear Signals For Early Damage Detection Based On The Spectral Kurtosis

机译：基于谱峰度的齿轮信号早期损伤检测最优滤波

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摘要

In this paper, we propose a methodology for the enhancement of small transients in gear vibration signals in order to detect local tooth faults, such as pitting, at an early stage of damage. We propose to apply the optimal denoising (Wiener) filter based on the spectral kurtosis (SK). The originality is to estimate and apply this filter to the gear residual signal, as classically obtained after removing the mesh harmonics from the time synchronous average (TSA). This presents several advantages over the direct estimation from the raw vibration signal: improved signaloise ratio, reduced interferences from other stages of the gearbox and easier detection of excited structural resonance(s) within the range of the mesh harmonic components. From the SK-based filtered residual signal, called SK-residual, we define the local power as the smoothed squared envelope, which reflects both the energy and the degree of non-stationarity of the fault-induced transients. The methodology is then applied to an industrial case and shows the possibility of detection of relatively small tooth surface pitting (less than 10%) in a two-stage helical reduction gearbox. The adjustment of the resolution for the SK estimation appears to be optimal when the length of the analysis window is approximately matched with the mesh period of the gear. The proposed approach is also compared to an inverse filtering (blind deconvolution) approach. However, the latter turns out to be more unstable and sensitive to noise and shows a lower degree of separation, quantified by the Fisher criterion, between the estimated diagnostic features in the pitted and unpitted cases. Thus, the proposed optimal filtering methodology based on the SK appears to be well adapted for the early detection of local tooth damage in gears.
机译：在本文中，我们提出了一种用于增强齿轮振动信号中小的瞬态的方法，以便在损坏的早期阶段检测出局部的齿部故障，例如点蚀。我们建议基于频谱峰度（SK）应用最佳降噪（Wiener）滤波器。独创性是估计该滤波器并将其应用于齿轮残差信号，这是从时间同步平均值（TSA）中去除网格谐波后经典获得的结果。与直接根据原始振动信号进行估算相比，这具有几个优点：改进的信噪比，降低了齿轮箱其他级的干扰以及在网状谐波分量范围内更容易检测到激发的结构共振。从基于SK的滤波残差信号（称为SK残差），我们将局部功率定义为平滑的平方包络，它既反映了能量，又反映了故障诱发的瞬变的非平稳程度。该方法随后应用于工业案例，并显示了在两级螺旋减速齿轮箱中检测到相对较小的齿表面点蚀（小于10％）的可能性。当分析窗口的长度与齿轮的啮合周期大致匹配时，SK估计的分辨率调整似乎是最佳的。还将所提出的方法与逆滤波（盲反卷积）方法进行了比较。但是，后者在有凹痕和无凹痕的情况下估计的诊断特征之间比较不稳定，并且对噪声更敏感，并且显示出较低的分离度（通过Fisher准则量化）。因此，所提出的基于SK的最佳过滤方法似乎很适合早期检测齿轮中的局部齿损坏。

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