EEMD模糊熵和灰色关联度在齿轮故障诊断中的应用

Proceedings of the 2nd International Conference on Artificial Intelligence and Advanced Manufacture Pub Date : 2020-10-15 DOI:10.1145/3421766.3421829

Wenbin Zhang, Y. Pu

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引用次数: 0

摘要

针对齿轮测量信号受噪声干扰不能准确反映故障特征的问题，提出了一种新的齿轮箱故障识别方法。采用秩序形态滤波器，消除了原始信号中噪声的影响。然后利用集成经验模态分解(EEMD)方法对去噪后的数据进行分解。从这些分解结果中选择一些有用的分量并计算模糊熵。然后采用灰色关联度法对不同的齿轮故障类型进行识别。识别结果表明了该方法的有效性。

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Application of EEMD Fuzzy Entropy and Grey Relation Degree in Gear Fault Diagnosis

Focusing on the gear measured signal can not accurately reflect the fault characteristics due to noise interference, a novel way was introduced for gearbox fault recognition in the article. By using the rank-order morphological filter, the influence of noises was eliminated from the original signal. Then the ensemble empirical mode decomposition (EEMD) method was utilized for decomposition of the de-noised data. From these decomposition results, some useful components were selected and calculated the fuzzy entropy. Then the grey relation degree method was used to recognize different gear fault type. Recognition results express the effectiveness of the proposed method.

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Proceedings of the 2nd International Conference on Artificial Intelligence and Advanced Manufacture

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