中国体育彩票福建31选7开奖结果:
[1]袁兵余佳翰,邹永向.基于EEMD-SVM的液压泵故障诊断[J].起重运输机械,2019,(20):90.
 Fault Diagnosis of Hydraulic Pump Based on EEMD-SVM[J].,2019,(20):90.
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基于EEMD-SVM的液压泵故障诊断()
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《起重运输机械》[ISSN:1001-0785/CN:11-1888/TN]

卷:
期数:
2019年20期
页码:
90
栏目:
故障诊断
出版日期:
2019-11-30

文章信息/Info

Title:
Fault Diagnosis of Hydraulic Pump Based on EEMD-SVM
作者:
袁兵 102 102); font-family: Arial Verdana sans-serif; font-size: 12px; background-color: rgb(255 255 255);">余佳翰邹永向
文献标志码:
A
摘要:
为了提高利用液压泵振动信号进行故障诊断的准确率和减小诊断时间,使用了集合经验模态分解(EEMD)的方式来提取振动信号特征,并将其作为液压泵故障诊断的数据集。在此基础上利用支持向量机(SVM)与深度神经网络(DNN)进行故障诊断,最后通过验证数据集检验了模型诊断故障的准确程度。结果表明,EEMD-SVM在液压泵故障诊断方面具有较好的性能,与神经网络故障诊断模型相比,支持向量机模型在液压泵的故障诊断方面具有更高的准确率和更短的诊断时间。
Abstract:
In order to improve the accuracy reduce the diagnosis time of hydraulic pump fault diagnosis by using vibration signal, the ensemble empirical mode decomposition (EEMD) method was used to extract vibration signal acteristics, it was used as the data set of hydraulic pump fault diagnosis. On this basis, support vector machine (SVM) deep neural network (DNN) were used for fault diagnosis. Finally, the accuracy of model fault diagnosis was verified by validating data sets. The results show that EEMD-SVM has better performance in fault diagnosis of hydraulic pumps. Compared with neural network fault diagnosis model, support vector machine model has higher accuracy shorter diagnosis time in fault diagnosis of hydraulic pumps.
更新日期/Last Update: 2019-12-02