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Research on early fault diagnosis for rolling bearing based on permutation entropy algorithm

机译:基于置换熵算法的滚动轴承早期故障诊断研究

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Permutation Entropy (PE) is a new subject which talks about the scrambling and non-linearity of complex system, which has been widely studied in recent years. This paper aims to introduce the basic algorithm of PE firstly, then verify PE using simulated signal, which shows that PE is feasible for fault diagnosis. Finally, the whole life vibration data of a rolling bearing is taken as an example, comparing with variation characteristics of mean square value obtained from the vibration signal, it is well proved that the early abnormity character of vibration signal could be successfully detected by the PE early before the fault occurred. The algorithm of PE was very simple and effective for early fault diagnosis, which supported the feasible idea for the on-line fault diagnosis, so this method will play a key role in the predictive maintenance.
机译:置换熵(Permutation Entropy,PE)是一门谈论复杂系统的扰动和非线性问题的新话题,近年来已被广泛研究。本文的目的是首先介绍PE的基本算法,然后通过仿真信号对PE进行验证,证明PE在故障诊断中是可行的。最后,以滚动轴承的全寿命振动数据为例,与从振动信号获得的均方值变化特征进行比较,很好地证明了PE可以成功地检测出振动信号的早期异常特征。故障发生之前。 PE算法对故障的早期诊断非常简单有效,为在线故障诊断提供了可行的思路,因此该方法在预测维护中将发挥关键作用。

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