基于CSA优化RBF网络的液压泵典型故障诊断研究
Research on Typical Fault Diagnosis of Hydraulic Pump Based on CSA Optimised RBF Network
王启晗 1邢锡华2
作者信息
- 1. 新乡职业技术学院智能制造学院,河南 新乡 453006
- 2. 视微影像(河南)科技有限公司,河南 洛阳 471003
- 折叠
摘要
液压泵具有高负载的特性,在液压机上广泛应用.为了提高液压泵典型故障诊断精度,设计了一种基于布谷鸟搜索算法(CSA)优化径向基函数(RBF)网络的液压泵典型故障诊断方法,然后对对液压泵典型故障进行实验测试.研究结果表明:对比PSO-RBF与UKF-RBF发现,CSA算法在寻优能力方面获得了明显提升,表现出来很高的收敛效果.CSA-RBF故障诊断水平最高,故障诊断缩减了总体耗时,检测平均精度能够达到97％以上,具有很高的精度保障.该方法有助于提高液压泵的使用寿命,为后续的参数优化奠定一定的理论基础.
Abstract
Hydraulic pumps have high load characteristics and are widely used in hydraulic machines.In order to improve the typical fault diagnosis accuracy of hydraulic pump,a typical fault diagnosis method of hydraulic pump based on cuckoo search algorithm(CSA)optimised radial basis function(RBF)network is designed,and then experimental tests are carried out on the typical faults of hydraulic pump.The results show that:comparing PSO-RBF and UKF-RBF,it is found that the CSA algorithm has gained a significant improvement in the optimisation searching ability,and exhibits a high convergence effect.The CSA-RBF has the highest level of fault diagnosis,and the fault diagnosis has shrunk the overall time-consuming,and the average accuracy of the detection is able to reach more than 97％,which is a very high guarantee of accuracy.This method helps to improve the service life of the hydraulic pump and lays a certain theoretical foundation for the subsequent parameter optimisation.
关键词
液压泵/故障诊断/布谷鸟搜索算法/径向基函数Key words
hydraulic pump/fault diagnosis/cuckoo search algorithm/radial basis function引用本文复制引用
出版年
2024
NETL
NSTL
万方数据