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Fault Classify of Rolling Bearing Based on Time-frequency Generalized Dimension of Vibration Signal and ANFIS

机译:基于振动信号和ANFI的时频广泛尺寸的滚动轴承故障分类

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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,而且还增加了信号几何特征和局部缩放行为的细度。基于多分形尺寸计算的滚动轴承振动信号的时间频率矩阵,可以提取振动信号时频域的能量分布特性,然后在信号分类中使用自适应模糊神经网络(ANFIS)。实验表明,该方法可以实现滚动轴承有效的故障分类,可行的工程应用。

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