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Fault classify of rolling bearing based on time-frequency generalized dimension of vibration signal and ANFIS

机译:基于振动信号的时频广义维和ANFIS的滚动轴承故障分类

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摘要

Research shows that multi-fractal can not only exhibit the singular probability distribution form of the fractal signal completely, but also increase the fine level of signal geometrical characteristics and local scaling behavior. Based on multi fractal dimension calculation of time frequency matrix of vibration signal of rolling bearing in this paper, energy distribution characteristics of time-frequency domain of vibration signal could be extracted, then adaptive fuzzy neural network (ANFIS) was used in signal classification. Experiments showed that this method can realize fault classify of rolling bearing effectively, it is feasible in engineering application.
机译:研究表明,多重分形不仅可以完全表现出分形信号的奇异概率分布形式,而且可以提高信号几何特性和局部缩放行为的精细水平。本文基于滚动轴承振动信号时频矩阵的分形维数计算,提取振动信号时频域的能量分布特征,然后采用自适应模糊神经网络(ANFIS)进行信号分类。实验表明,该方法能有效实现滚动轴承的故障分类,在工程应用中是可行的。

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