一种基于数字孪生的风机主轴承故障诊断模型  

A Model for Wind Turbine Main Bearing Fault Diagnosis Based on the Digital Twin

作　　者：蒋亚丹 徐茹枝[1] 关志涛[1] 王金香 JIANG Yadan;XU Ruzhi;GUAN Zhitao;WANG Jinxiang(School of Control and Computer Engineering,North China Electric Power University,Changping District,Beijing 102206,China)

机构地区：[1]华北电力大学控制与计算机工程学院,北京昌平区102206

出　　处：《电力信息与通信技术》2025年第4期9-17,共9页Electric Power Information and Communication Technology

基　　金：国家自然科学基金项目(62372173)。

摘　　要：针对新型电力系统下新建风电场训练样本不足导致风机轴承故障诊断精确度较低、无法实时状态监测等问题,文章提出一种基于数字孪生及动力学模型和迁移学习融合的风机主轴承故障诊断模型。针对新建风电场数据不足、无法无损产生故障数据的问题,该模型利用MATLAB/Simulink构建风机主轴承的动力学孪生模型在不同工况下生成仿真数据,模拟风机主轴承实际运行中难以获得的故障数据;针对传统轴承故障诊断需进行数据预处理无法实现原始振动信号端到端实时故障诊断的问题,提出一种基于深度卷积网络和改进残差块的算法模型,加入BN层和dropout层,规避数据预处理造成的层层误差;针对从虚拟实体到物理现实的迁移问题,利用数字孪生技术模拟出的大量平衡数据集对模型进行预训练,训练后的模型通过迁移学习投入到风机主轴承实际运行中进行故障诊断。实验结果表明,以凯斯西储大学数据集作为风机主轴承真实运行数据为例,验证所提方法的可行性,所提出的风机主轴承数字孪生模型方法故障诊断准确率达到99.7%,较未改进前的非数字孪生传统CNN方法诊断准确率提升22%。In order to solve the problems such as low accuracy of fan bearing fault diagnosis and inability of real-time con-dition monitoring due to insufficient training samples of newly built wind farms in new power system,a digital twin model of fan main bearing fault diagnosis based on the fusion of model knowledge and data drive is pro-posed in this paper.Firstly,the twin model of the main bearing of the fan is constructed by MATLAB/Simulink to generate simulation data under different working conditions,so as to simulate the fault data that is difficult to obtain in the actual operation of the main bearing of the fan,so as to solve the problem of insufficient data of the newly built wind farm and unable to generate fault data lossless.Then,a fault diagnosis model based on deep convolutional networks and improved residual blocks is proposed.By adding the BN layer and dropout layer,end-to-end real-time fault diagnosis of one-dimensional original vibration signals can be realized without data preprocessing.Finally,a large number of balanced data sets simulated by digital twinning technology were used for pre-training of the model,and the trained model was put into the actual operation of the fan main bearing for fault diagnosis through transfer learning.The experiment shows that taking CWRU data set as the real operation data of fan main bearing as an example,the feasibility of the proposed metho d is verified.The fault diagnosis accuracy of the proposed digital twin model method of wind turbine main bearing is 99.7%,marking a 22%improvement over the traditional CNN method without digital twin enhancement.

关 键 词：数字孪生 动力学模型 迁移学习 风机主轴承 故障诊断 

分 类 号：TN915.853[电子电信—通信与信息系统]