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Fault Diagnosis of Rolling Bearing Based on Wavelet Package Transform and Ensemble Empirical Mode Decomposition

机译:基于小波包变换和集合经验模态分解的滚动轴承故障诊断。

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摘要

Rolling bearing is widely used in rotating mechanical system, and its operating state has great influence on accuracy, reliability, and the life of the whole mechanical system. Therefore, fault diagnosis of rolling bearing is indispensible for the health monitoring in rotating machinery system. Wavelet package transform (WPT) and envelope demodulation have been common methods in diagnosis of bearing fault, but the precision of diagnostic results is limited by the degree of damages on bearing. In this paper, a method based on WPT and ensemble empirical mode decomposition (EEMD) is proposed to detect the fault of rolling bearing and solve this problem. According to simulation and experimental results, it is effective in fault diagnosis of rolling bearing and is better than the method based on WPT and envelope spectrum while the faults get more serious.
机译:滚动轴承广泛用于旋转机械系统中,其工作状态对整个机械系统的精度,可靠性和寿命有很大影响。因此,滚动轴承的故障诊断对于旋转机械系统的健康监测是必不可少的。小波包变换(WPT)和包络解调是轴承故障诊断的常用方法,但诊断结果的精度受到轴承损坏程度的限制。提出了一种基于WPT和集成经验模态分解(EEMD)的方法来检测滚动轴承的故障并解决该问题。根据仿真和实验结果,该方法在滚动轴承故障诊断中是有效的,优于基于WPT和包络谱的方法,而且故障更加严重。

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