Novel spectral kurtosis technology for adaptive vibration condition monitoring of multi-stage gearboxes
Novel spectral kurtosis technology for adaptive vibration condition monitoring of multi-stage gearboxes
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DOI：
10.1784/insi.2016.58.8.409
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发表时间：
2016-08-01
期刊：
影响因子：
1.1
通讯作者：
Zippo, A.
中科院分区：
文献类型：
--
作者：
Gelman, L.;Chandra, N. Harish;Zippo, A.
本文将小波谱峰度（WSK）技术应用于齿轮齿故障的早期诊断。小波谱峰度技术的两种变体，即变分辨率WSK和恒分辨率WSK，被用于点蚀齿轮故障的诊断。通过对齿轮啮合频率进行滤波得到的齿轮残差信号作为SK算法的输入。与传统的基于傅立叶变换（FT）的SK相比，使用基于小波的SK技术的优势是通过估计牙齿的诊断特征的Fisher标准来证实的。最终诊断决策采用基于加权多数原则的三阶段决策技术。对每一种SK技术的正确诊断概率进行估计，以便进行比较。对小波谱峰度技术和决策技术的性能进行了详细的实验研究。
In this paper, the novel wavelet spectral kurtosis (WSK) technique is applied for the early diagnosis of gear tooth faults. Two variants of the wavelet spectral kurtosis technique, called variable resolution WSK and constant resolution WSK, are considered for the diagnosis of pitting gear faults. The gear residual signal, obtained by filtering the gear mesh frequencies, is used as the input to the SK algorithm. The advantages of using the wavelet-based SK techniques when compared to classical Fourier transform (FT)-based SK is confirmed by estimating the toothwise Fisher's criterion of diagnostic features. The final diagnosis decision is made by a three-stage decision-making technique based on the weighted majority rule. The probability of the correct diagnosis is estimated for each SK technique for comparison. An experimental study is presented in detail to test the performance of the wavelet spectral kurtosis techniques and the decision-making technique.