An enhanced Kurtogram method for fault diagnosis of rolling element bearings
An enhanced Kurtogram method for fault diagnosis of rolling element bearings
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DOI：
10.1016/j.ymssp.2012.10.003
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发表时间：
2013-02-01
影响因子：
8.4
通讯作者：
Tsui, Kwok Leung
中科院分区：
文献类型：
--
作者：
Wang, Dong;Tse, Peter W.;Tsui, Kwok Leung
Kurtogram是基于经过短时傅里叶变换(STFT)滤波的时间信号的峰度，已被证明在轴承故障诊断中是有用的。为了更有效地提取暂态冲击信号，小波包变换被认为是信号分解SIFT的一种替代方法。虽然基于时间信号的峰度在某些条件下是有效的，但在低信噪比和非高斯噪声存在的情况下，其性能较低。本文提出了一种改进的库尔托图，其主要创新点是根据不同深度小波包节点提取的信号包络的功率谱计算峰度。信号包络的功率谱定义了信号的稀疏表示，峰度测量了稀疏表示的突出度。这一增强的库尔托图有助于确定共振频带的位置，以便通过包络分析进一步解调。然后，包络信号的频率特征可用于通过识别其特征频率来确定影响轴承的故障类型。在许多情况下，离散频率噪声总是存在，并且可能掩盖轴承的弱故障。在执行增强的库尔托格拉姆图之前，通常最好通过使用自回归滤波来去除这种离散频率噪声。最后，我们用大量的模拟轴承故障信号和从实验电机获得的三个实际轴承故障信号来验证所提出的改进的有效性。结果表明，该方法和改进的库尔托图方法对各种轴承故障的检测都是有效的。(C)2012爱思唯尔有限公司保留所有权利。
The Kurtogram is based on the kurtosis of temporal signals that are filtered by the short-time Fourier transform (STFT), and has proved useful in the diagnosis of bearing faults. To extract transient impulsive signals more effectively, wavelet packet transform is regarded as an alternative method to SIFT for signal decomposition. Although kurtosis based on temporal signals is effective under some conditions, its performance is low, in the presence of a low signal-to-noise ratio and non-Gaussian noise. This paper proposes an enhanced Kurtogram, the major innovation of which is kurtosis values calculated based on the power spectrum of the envelope of the signals extracted from wavelet packet nodes at different depths. The power spectrum of the envelope of the signals defines the sparse representation of the signals and kurtosis measures the protrusion of the sparse representation. This enhanced Kurtogram helps to determine the location of resonant frequency bands for further demodulation with envelope analysis. The frequency signatures of the envelope signal can then be used to determine the type of fault that has affected a bearing by identifying its characteristic frequency. In many cases, discrete frequency noise always exists and may mask the weak bearing faults. It is usually preferable to remove such discrete frequency noise by using autoregressive filtering before the enhanced Kurtogram is performed. At last, we used a number of simulated bearing fault signals and three real bearing fault signals obtained from an experimental motor to validate the efficiency of these proposed modifications. The results show that both the proposed method and the enhanced Kurtogram are effective in the detection of various bearing faults. (C) 2012 Elsevier Ltd All rights reserved.