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Performance enhancement of ensemble empirical mode decomposition

机译:整体经验模态分解的性能增强

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Ensemble empirical mode decomposition (EEMD) is a newly developed method aimed at eliminating mode mixing present in the original empirical mode decomposition (EMD). To evaluate the performance of this new method, this paper investigates the effect of two parameters pertinent to EEMD: the amplitude of added white noise and the number of ensemble trials. A test signal with mode mixing that mimics realistic bearing vibration signals measured on a bearing test bed was developed to enable quantitative evaluation of the EEMD and provide guidance on how to choose the two parameters appropriately for bearing signal decomposition. Subsequently, a modified EEMD (MEEMD) method is proposed to reduce the computational cost of the original EEMD method as well as improving its performance. Numerical evaluation and systematic study using vibration data measured on an experimental bearing test bed verified the effectiveness and computational efficiency of the proposed MEEMD method for bearing defect diagnosis.
机译:集成经验模式分解(EEMD)是一种新开发的方法,旨在消除原始经验模式分解(EMD)中存在的模式混合。为了评估这种新方法的性能,本文研究了与EEMD有关的两个参数的效果:增加的白噪声的幅度和合奏试验的次数。开发了一种具有模式混合的测试信号,该信号模拟了在轴承测试台上测得的实际轴承振动信号,从而能够对EEMD进行定量评估,并为如何正确选择两个参数进行轴承信号分解提供指导。随后,提出了一种改进的EEMD(MEEMD)方法,以减少原始EEMD方法的计算成本并提高其性能。使用在轴承试验台上测得的振动数据进行的数值评估和系统研究证明了所提出的MEEMD方法用于轴承缺陷诊断的有效性和计算效率。

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