China Mechanical Engineering ›› 2021, Vol. 32 ›› Issue (07): 799-805.DOI: 10.3969/j.issn.1004-132X.2021.07.006

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Identification of Fault Degree of Oil Filter Blockage in Electro-hydraulic Actuator Based on an Improved PCA-SOM

CHEN Huanguo1;LIU Peijun1;YU Hang1;XIAO Xue2   

  1. 1.Zhejiang Provinces Key Laboratory of Reliability Technology for Mechanical and Electrical Product,Zhejiang Sci-Tech University,Hangzhou,310018
    2.Beijing Institute of Precision Mechatronics and Controls,Beijing,100076
  • Online:2021-04-10 Published:2021-04-16



  1. 1. 浙江理工大学浙江省机电产品可靠性技术研究重点实验室,杭州,310018
    2. 北京精密机电控制设备研究所,北京,100076
  • 作者简介:陈换过,女,1977年生,教授。研究方向为机械零部件可靠性,结构健康监控、结构动力学、故障预测与健康管理。E-mail:。
  • 基金资助:

Abstract: In view of the oil filter plugging faults of EHA, to using adjustable ball head oil plug was proposed to preset different degrees of plugging conditions for data collection, and based on the traditional SOM, PCA was introduced to revise each dimensional coefficient of the neuron competition domain values by using the contribution rates of each principal component, as well as proposed an improved PCA-SOM neural network to identify the blockage states of the system. The results show that compared with the traditional SOM neural network and PCA-SOM neural network, the improved PCA-SOM neural network has higher applicability in EHAs oil filter blockage fault diagnosis, which is embodied in that the clustering effectiveness is improved while the accuracy and training speed of the model are also promoted.

Key words: electro-hydraulic actuator(EHA), improved principal component analysis(PCA)-self-organizing map(SOM) neural network, oil filter blockage, fault diagnosis

摘要: 针对电静压伺服作动器(EHA)的油滤堵塞故障,提出利用可调式球头油堵预置不同程度的油滤堵塞工况进行数据采集,并在传统自组织映射神经网络(SOM)的基础上,引入主成分分析(PCA)法,利用各元主成分贡献率对神经元竞争域值各维系数进行修订,提出了改进PCA-SOM神经网络对系统堵塞状态进行判识。研究结果表明,与传统SOM神经网络和PCA-SOM神经网络相比,改进PCA-SOM神经网络在提高聚类效果的同时,提高了模型的准确率和训练速度,在EHA的油滤堵塞故障诊断中有更好的适用性。

关键词: 电静压伺服作动器, 改进主成分分析法-自组织映射神经网络, 油滤堵塞, 故障诊断

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