机电工程技术2024，Vol.53Issue(2)：103-106,4.DOI:10.3969/j.issn.1009-9492.2024.02.022
基于改进生成对抗网络的风电机组主轴承故障诊断研究
Research on Fault Diagnosis of Main Bearing of Wind Turbine Based on Improved Generative Adversarial Network
摘要
Abstract
The wind turbine main bearing is the key component of the unit,with high maintenance costs.In order to provide accurate diagnosis support,an improved fault diagnosis method is proposed based on the generative adversarial network(GAN).The original GAN model and bearing data set are described.In the background of extremely weak negative sample data of wind farm,a depth learning network framework is designed by stacking multiple constrained sparse automatic encoders(CSAE)to extract the depth features of samples,which is a method for constructing functions with better expressibility.Combining GAN with DCSAE,the multilayer perception network is used as generating network model,and DCSAE is used as discriminating network model to form the training method of bearing deterioration model.By updating the optimized deterioration model,a high-precision diagnosis model of bearing deterioration is obtained.The adaptive diagnosis of bearing deterioration can be realized by transferring the auxiliary data set and the target data set to form a diagnosis model with strong generalization ability and robustness.Through the example analysis,by using the proposed improved method,the goal of efficient learning of the distribution features of the original data can be realized,and the distribution model of a few kinds of fault data can be constructed,the improved model still shows better diagnostic ability in different small sample scenarios.关键词
生成对抗网络/风电机组/主轴承/故障诊断Key words
generative adversarial network(GAN)/wind turbine/main bearing/fault diagnosis分类
信息技术与安全科学引用本文复制引用
颜毅斌,陈清化,吉天平,李晟方..基于改进生成对抗网络的风电机组主轴承故障诊断研究[J].机电工程技术,2024,53(2):103-106,4.基金项目
湖南省自然科学基金资助项目(2022JJ60074) （2022JJ60074）
湖南省教育厅资助科研项目(20C1226) （20C1226）
