摘要
单向阀作为液压系统重要元件,发生内泄漏故障时影响液压系统的工作效率和设备的安全稳定运行。针对单向阀内泄漏预测,提出了一种基于多源信号和布谷鸟搜索支持向量回归(CS-SVR)的单向阀内泄漏无损预测方法。首先建立单向阀内泄漏检测实验平台,获得不同工况不同故障特征下单向阀内泄漏的振动信号和声发射信号。然后利用小波包能量分析方法,提取两种信号最优频带重构信号的均方根值作为预测变量,并结合阀前压力建立基于布谷鸟算法优化的支持向量机回归(CS-SVR)的多源信号预测模型。最后,将模型进行对比分析。结果表明,布谷鸟搜索算法相对于网格搜索法(GS)和粒子群算法(PSO)对SVR模型参数寻优优势更大;基于多源信号输入的预测方法相对于单源输入的预测方法精确程度更高,同时该方法能实现不同压力、不同故障类别、不同故障程度的单向阀内泄率预测,平均相对误差为8.97%鲁棒性较高,为阀内泄漏无损检测应用技术的开发奠定了基础。
As an important component of the hydraulic system,check valve affects the work efficiency of the hydraulic system and the safe and stable operation of the equipment when internal leakage occurs.Aiming at the leakage prediction in check valve,a non-destructive leakage prediction method in check valve based on multi-source signal and cuckoo search support vector regression(CS-SVR)is proposed.Firstly,a leakage detection experimental platform in the check valve is established,and the vibration signal and acoustic emission signal leaking in the check valve under the different working conditions and the different fault characteristics are obtained on the platform.Secondly,wavelet packet energy analysis method is used to extract the root mean square(RMS)of optimal frequency band reconstruction signals which are as the predictors with the inlet pressure to establish a multi-source signal prediction model based in CS-SVR.Finally,with compared and analyzed the models,the results show that the cuckoo search(CS)has a greater advantage over the grid search(GS)and particle swarm optimization(PSO)for SVR model parameters.Prediction methods based on multi-source signal inputs are more accurate than single-source input prediction methods,and the proposed method can realize the prediction of the internal leakage rate of the check valve with different pressure,different fault categories and different fault degrees.The proposed method average relative error is 8.97%and has high robustness,which lays a foundation for the development of non-destructive testing application technology for valve leakage.
作者
李威
童成彪
吴家腾
伍奕桦
Li Wei;Tong Chengbiao;Wu Jiateng;Wu Yihua(College of Mechanical and Electrical Engineering,Hunan Agricultural University,Changsha 410128,China)
出处
《电子测量与仪器学报》
CSCD
北大核心
2023年第1期222-230,共9页
Journal of Electronic Measurement and Instrumentation
基金
湖南省自然科学基金(2020JJ4045)
湖南省重点研发项目(2022NK2028)资助
作者简介
通信作者:童成彪,2017年于湖南大学获得博士学位,现为湖南农业大学教授,主要研究方向为机械设备故障诊断与性能优化。E-mail:tongcb@163.com;李威,2020年与湖南农业大学获得学士学位,现为湖南农业大学硕士研究生,主要研究方向为机械故障诊断。E-mail:847553546@qq.com
