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Feature Extraction of Vibration of Centrifugal Fan Based on LLE

机译:基于LLE的离心式风扇振动特征提取

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In order to improve classification ability and diagnostic accuracy of centrifugal fan signals, a new feature extraction method from fault signals of centrifugal fan vibration based on manifold learning method (MLM) that is a kind of reduction method of data dimension is proposed in this paper.The MLM is able to remain nonlinear information of original signal, to improve the classification and diagnostic ability of fault better than traditional reducing dimension methods. The results in this paper show that, fault feature information of centrifugal fan vibration is extracted effectively by the MLM and the fault feature information of different types are separated effectively in themselves areas. The diagnostic accuracy by feature extracted by the MLM is significantly higher than by the wavelet packet analysis method.
机译:为了提高离心风扇信号的分类能力和诊断准确性,本文提出了一种基于歧管学习方法(MLM)的离心风机振动故障信号的新特征提取方法,即是一种数据尺寸的一种减少方法。 MLM能够保持原始信号的非线性信息,提高故障的分类和诊断能力优于传统的还原尺寸方法。本文的结果表明,通过MLM有效地提取了离心风扇振动的故障特征信息,并且在自己的区域中有效地分离不同类型的故障特征信息。由MLM提取的特征的诊断精度明显高于小波分组分析方法。

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