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。
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 EHA's 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.
作者
陈换过
刘培君
俞杭
肖雪
CHEN Huanguo;LIU Peijun;YU Hang;XIAO Xue(Zhejiang Province's Key Laboratory of Reliability Technology for Mechanical and Electrical Product,Zhejiang Sci-Tech University,Hangzhou,310018;Beijing Institute of Precision Mechatronics and Controls,Beijing,100076)
出处
《中国机械工程》
EI
CAS
CSCD
北大核心
2021年第7期799-805,共7页
China Mechanical Engineering
基金
国家自然科学基金(51975535)
NSFC-浙江两化融合项目(U1709210)
浙江省重点研发计划(2019C03108)。
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
electro-hydraulic actuator(EHA)
improved principal component analysis(PCA)-self-organizing map(SOM)neural network
oil filter blockage
fault diagnosis
