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首页> 外文期刊>Mechanical systems and signal processing >Enhancement of autoregressive model based gear tooth fault detection technique by the use of minimum entropy deconvolution filter
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Enhancement of autoregressive model based gear tooth fault detection technique by the use of minimum entropy deconvolution filter

机译：通过使用最小熵反卷积滤波器增强基于自回归模型的齿轮故障检测技术

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摘要

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.
机译：本文提出使用最小熵反卷积（MED）技术来增强现有的基于自回归（AR）模型的滤波技术检测齿轮中局部故障的能力。与传统使用的残差分析技术相比，AR滤波技术已被证明在检测局部齿轮故障方面具有优势。 AR滤波器技术基于从同步信号平均技术（SSAT）获得的来自一个齿轮的信号频谱中减去常规齿轮啮合信号，该信号由齿啮合谐波和紧邻的边带表示。现有的AR滤波器技术表现良好，但是基于自相关测量，因此对可用于区分噪声和脉冲的相位关系不敏感。 MED技术可以借助信号（尤其是峰度）的高阶统计（HOS）特性来利用相位信息，以增强检测出现的齿轮故障的能力。本文提出的实验结果验证了AR和MED组合过滤技术在检测齿轮剥落和齿角裂纹方面的优越性能。

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