气动调节阀粘滞故障检测与参数辨识方法研究  

Detection and parameter identification of stiction in control valves

作　　者：向方娜 管桉琦 林振浩 金志江[1,3] 钱锦远[1] XIANG Fangna;GUAN Anqi;LIN Zhenhao;JIN Zhijiang;QIAN Jinyuan(Institute of Process Equipment,Zhejiang University,Hangzhou 310027,China;Valvever Intelligent Technology Co.,Ltd.,Hangzhou 310058,China;Institute of Wenzhou,Zhejiang University,Wenzhou 325036,China)

机构地区：[1]浙江大学化工机械研究所,杭州310027 [2]阀源智能科技(杭州)有限公司,杭州310058 [3]浙江大学温州研究院,浙江温州325036

出　　处：《流体机械》2024年第8期23-30,共8页Fluid Machinery

基　　金：国家重点研发计划项目(2021YFB2011300);国家自然科学基金项目(52175067)。

摘　　要：为了研究气动调节阀的粘滞特性问题,以CHEN粘滞模型作为阀门粘滞特性问题的基础模型,提出一种阀门粘滞故障的检测与改进的参数辨识方法。通过搭建粘滞故障试验台,模拟实际情况中发生不同程度粘滞时的阀杆运动状态,揭示阀门粘滞发生的机理。使用随机森林算法对振荡源进行分类,实现对粘滞故障的检测。使用基于Hammerstein模型的粒子群优化算法求解粘滞参数最优解,提出粘滞双参数取值范围的确定方法,实现欠补偿、无补偿和过补偿3种状态下的粘滞参数辨识。结果表明:阀门粘滞程度与阀杆所受动静摩擦力有关;在不考虑外界因素的影响下,提出的粘滞检测方法对4种振荡源的分类准确率达到99.0268%;提出的改进的粘滞参数辨识方法对不同大小粘滞参数的辨识结果误差达到7%以内。研究成果为阀门粘滞故障的检测和参数辨识提供了理论方法,对粘滞模型的改进具有实际参考价值。To discuss the stiction characteristics of pneumatic control valves,a method of valve stiction detection and parameter identification was proposed with CHEN's stiction model as the basic stiction model.A stiction experimental platform was built to simulate the movement of valve stem with different degrees of stiction to reveal the generation mechanism of valve stiction.The random forest algorithm was used to classify oscillation sources and detect stiction.The particle swarm optimization algorithm based on Hammerstein model was used to get the optimal solution of stiction parameters.Besides,a method for determining the range of values of double stiction parameters was proposed.The proposed method has achieved the stiction parameter identification in three conditions,i.e.under-compensation,no compensation and over-compensation.The results indicate that the degree of valve stiction is related to dynamic and static friction force on the valve stem.If the effects of external factors are not considered,the proposed method of stiction detection achieves a classification accuracy of 99.0268%.The improved method of stiction parameter identification achieves an error of less than 7%.The research results provide theoretical methods for valve stiction detection and parameter identification,and provide practical reference value for the improvement of stiction models.

关 键 词：气动调节阀 粘滞特性 粘滞检测 粘滞参数辨识 随机森林算法 粒子群优化算法 

分 类 号：TH138.52[机械工程—机械制造及自动化]