首页|基于离心泵数字孪生流场云图的叶轮故障诊断方法与应用

基于离心泵数字孪生流场云图的叶轮故障诊断方法与应用

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随着工业技术的发展,离心泵的健康诊断与维护需求日益迫切,结合数字孪生和机器视觉技术,提出一种基于数字孪生流场云图的离心泵叶轮机械故障智能诊断方法.借助离心泵数字孪生模型来模拟叶轮叶片随机断裂故障的演化发展,生成具有不同故障特征的叶轮流场压力及速度云图;基于对Yolov5算法的学习训练,获得了压力和速度云图两类机器视觉模型,并结合统计分析实现了叶轮故障的初步诊断;进而考虑两类检测模型的优势互补特性,基于堆叠集成的思想将二者融合,以提升叶轮故障诊断的准确性.经实验验证,针对叶轮叶片的随机断裂故障,所提方法可达到0.99以上的诊断准确度,开发的离心泵叶轮机械故障智能诊断系统使所提方法得以落地应用.
Impeller fault diagnosis method and application based on digital twin flow field contour of centrifugal pump
With the development of industrial technology,the health diagnosis and maintenance of centrifugal pumps are increasingly urgent.Combining digital twin and machine vision technology,this paper proposed an intelligent impeller fault diagnosis method for centrifugal pumps based on a digital twin flow field cloud diagram.First of all,the digital twin model of the centrifugal pump was used to simulate the evolution of the random fracture for the impeller blades,and the pressure and velocity cloud diagrams of the impeller flow field with different fault characteristics were generated.Secondly,based on the learning and training of the Yolov5 algorithm,two kinds of machine vision models,namely pressure and velocity cloud diagrams,were obtained,and the preliminary diagnosis of impeller fault was realized by combining statistical analysis.Furthermore,the complementary advantages of the two types of detection models were considered,and the two types of detection models were combined based on the idea of stack integration to improve the accuracy of impeller fault diagnosis.The experimental verification shows that the intelligent fault diagnosis method for centrifugal pumps proposed in this paper has a diagnosis accuracy of more than 0.99 for the random fracture of impeller blades.The developed intelligent impeller fault diagnosis system for centrifugal pumps makes the method developed in this paper be applied to practical scenarios.

centrifugal pumpdigital twinsimpellermachine visionintelligent diagnosis

李亚洁、刘强、李炜

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兰州理工大学电气工程与信息工程学院,兰州 730050

兰州理工大学甘肃省工业过程先进控制重点实验室,兰州 730050

兰州理工大学电气与控制工程国家级实验教学示范中心,兰州 730050

离心泵 数字孪生 叶轮机械 机器视觉 智能诊断

2025

北京航空航天大学学报
北京航空航天大学

北京航空航天大学学报

北大核心
影响因子:0.617
ISSN:1001-5965
年,卷(期):2025.51(1)