Enhancement of autoregressive model based gear tooth fault detection technique by the use of minimum entropy deconvolution filter
Enhancement of autoregressive model based gear tooth fault detection technique by the use of minimum entropy deconvolution filter
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DOI：
10.1016/j.ymssp.2006.02.005
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发表时间：
2007-02-01
影响因子：
8.4
通讯作者：
Randall, R. B.
中科院分区：
文献类型：
--
作者：
Endo, H.;Randall, R. B.
本文提出使用最小熵反卷积（MED）技术来增强现有基于自回归（AR）模型的滤波技术检测齿轮局部故障的能力。事实证明，AR 滤波器技术在检测局部齿轮故障方面优于传统使用的残差分析技术。 AR 滤波器技术的基础是从通过同步信号平均技术 (SSAT) 获得的一个齿轮的信号频谱中减去由齿啮合谐波和紧邻边带表示的常规齿轮啮合信号。现有的 AR 滤波器技术性能良好，但基于自相关测量，因此对可用于区分噪声和脉冲的相位关系不敏感。 MED技术可以通过信号的高阶统计（HOS）特性，特别是峰度来利用相位信息，以增强检测新出现的轮齿故障的能力。本文提出的实验结果验证了 AR 和 MED 滤波组合技术在检测齿轮剥落和齿圆角裂纹方面的优越性能。 (c) 2006 Elsevier Ltd. 保留所有权利。
This paper proposes the use of the minimum entropy deconvolution (MED) technique to enhance the ability of the existing autoregressive (AR) model based filtering technique to detect localised faults in gears. The AR filter technique has been proven superior for detecting localised gear tooth faults than the traditionally used residual analysis technique. The AR filter technique is based on subtracting a regular gearmesh signal, as represented by the toothmesh harmonics and immediately adjacent sidebands, from the spectrum of a signal from one gear obtained by the synchronous signal averaging technique (SSAT). The existing AR filter technique performs well but is based on autocorrelation measurements and is thus insensitive to phase relationships which can be used to differentiate noise from impulses. The MED technique can make a use of the phase information by means of the higher-order statistical (HOS) characteristics of the signal, in particular the kurtosis, to enhance the ability to detect emerging gear tooth faults. The experimental results presented in this, paper validate the superior performance of the combined AR and MED filtering techniques in detecting spalls and tooth fillet cracks in gears. (c) 2006 Elsevier Ltd. All rights reserved.