噪声与振动控制2024，Vol.44Issue(3)：101-108,8.DOI:10.3969/j.issn.1006-1355.2024.03.016
针对汽轮机转子故障样本不足的典型故障检测方法研究
Study on Typical Fault Detection Method for Insufficient Turbine Rotor Fault Samples
摘要
Abstract
Currently,deep learning has achieved greater popularity and development in fault diagnosis of mechanical systems,and these intelligent models require a large amount of training data to ensure their generalization capability.However,the lack of or difficulty in obtaining actual turbine rotor fault data poses a new challenge for intelligent fault diagnosis.In this paper,a method for generating fault data and performing intelligent fault diagnosis based on numerical simulation of turbine rotors is proposed.By building a finite element model of the rotor,the fault information which reflects the operating condition of the rotor is generated to provide data samples for the intelligent model.Then,a high-precision rotor finite element model based on actual rotor signals is established,which can effectively solve the problem of insufficient fault samples and increase the accuracy of intelligent diagnosis.Through the combination of finite element technique and deep convolutional neural network,the proposed method can realize the end-to-end intelligent fault diagnosis of turbine rotors under the condition of insufficient fault samples and the difficulty of measuring some faults,meanwhile it has the advantages of high accuracy and strong robustness.关键词
故障诊断/汽轮机转子/数值模拟/故障样本不足/深度卷积神经网络/智能故障诊断Key words
fault diagnosis/steam turbine rotor/numerical simulation/insufficient fault sample/deep convolutional neural network/intelligent fault diagnosis分类
信息技术与安全科学引用本文复制引用
吴董炯,何群山..针对汽轮机转子故障样本不足的典型故障检测方法研究[J].噪声与振动控制,2024,44(3):101-108,8.基金项目
国家自然科学基金资助项目(51875332) （51875332）
