基于迭代平方包络分析的主轴承早期故障特征提取

Ming An-bo, Zhang Wei, He Hao-hao, Xie Xin-yu, Chu Fu-lei
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引用次数: 2

摘要

从收集到的旋转系统振动中提取轴承早期故障的微弱特征是航空发动机主轴承故障诊断的基础。为了监测主轴承的运行状态,通过提取轴承故障特征阶数,提出了一种新的轴承故障弱特征提取方法——迭代平方包络分析(ISEA)。通过模拟和实验,验证了该方法的有效性。结果表明,该方法能有效地消除转子产生的振动,提取轴承故障特征。与倒频谱预白化方法相比,其振幅和循环特征都比倒频谱预白化方法更接近真实值。因此,ISEA在轴承的弱特征提取方面比CPW方法更强大。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Incipient fault feature extraction of main bearing by iterative squared envelope analysis
Extracting weak features of incipient bearing fault from the collected vibration of rotating system is the basis of the fault diagnostics of main bearing in the aero engine. To monitor the running condition of the main bearing, a novel weak feature extraction method for bearing fault, named as iterative squared envelope analysis (ISEA) is proposed by extracting the fault characteristic orders of bearings. Both simulations and experiments, involving the outer and inner race faults, are performed to validate the efficacy of ISEA. It is shown that the ISEA can efficiently eliminate the vibrations produced by rotor and extract the bearing fault feature. Compared with the result obtained by the cepstrum pre-whiten method, both amplitude and cyclic feature can be reserved closer to the true values than that obtained by the cepstrum pre-whiten (CPW) method. Therefore, the ISEA is more powerful in the weak feature extraction of bearings than the CPW method.
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