PERIODICAL FEATURE EXTRACTION AND FAULT DIAGNOSIS FOR GEARBOX USING LOCAL CEPSTRUM TECHNOLOGY

机译：使用局部综科技术的齿轮箱周期特征提取和故障诊断

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Results of numerous studies and experiments show that cepstrum analysis has the ability of simplifying the equally spaced sideband feature in the spectrum and highlights the signal components of defects. However, for most cases of early gear failure, the periodic phenomenon is always buried in strong background noises and the interference of the rotating frequency with its harmonics. Moreover, the features would be further weakened by the average effect of Fourier transform after cepstrum processing. In this paper, an improved cepstrum method named local cepstrum is proposed. The detection principle of local cepstrum is mainly based on the part of spectrum information to enhance the capability of extracting periodical features of detected signals. Besides, the autocorrelation and extended Shannon Entropy Function are also involved enhancing the periodic impulsive features. In the end, only several distinct lines with larger magnitudes would be left in the local cepstrum, which is very effective for gear fault detection and identification. Both simulation and experimental analysis show that the proposed method, which is more sensitive to the gear failure compared with conventional cepstrum analysis, could partially eliminate the interference of background noise and extract the periodical features of premature failure signals effectively.

机译：大量的研究和实验的结果表明，倒频谱分析在光谱和亮点缺陷的信号分量简化等距间隔的边带特征的能力。然而，早期的齿轮故障的大多数情况下，周期性的现象总是埋在强背景噪声和其谐波的频率旋转的干扰。此外，该特征将进一步通过傅立叶变换的平均效应减弱倒谱处理之后变换。在本文中，提出了一个名为地方倒的改进方法，倒谱。本地对数倒频谱的检测原理主要是基于频谱信息的一部分，以提高提取探测到的信号的周期性特征的能力。此外，自相关和扩展信息熵功能也参与增强了周期脉冲的特征。最后，只有几个不同的线具有较大的幅度将在本地倒谱，这是齿轮故障检测和鉴定是非常有效的不留。两个模拟和实验分析表明，所提出的方法，这是与传统的倒频谱分析相比，齿轮故障更加敏感，可部分消除背景噪声的干扰和有效地提取过早失效信号的周期性特征。

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《ASME International Mechanical Engineering Congress and Exposition》|2016年||共6页
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B. LI; X. N. ZHANG;
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PERIODICAL FEATURE EXTRACTION AND FAULT DIAGNOSIS FOR GEARBOX USING LOCAL CEPSTRUM TECHNOLOGY

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