基于Nadam-TimeGAN和XGBoost的时序信号故障诊断方法
Fault diagnosis method of timing signal based on Nadam-TimeGAN and XGBoost
黑新宏 1高苗 2张宽 2费蓉 1邱原 1姬文江1
作者信息
- 1. 西安理工大学计算机科学与工程学院,陕西 西安 710048;陕西省网络计算与安全技术重点实验室,陕西 西安 710048
- 2. 西安理工大学计算机科学与工程学院,陕西 西安 710048
- 折叠
摘要
为了提高故障诊断模型在数据不平衡场景下的诊断性能和模型泛化能力,提出了一种基于Nadam-TimeGAN和XGBoost的时序信号故障诊断方法.首先对比基于LSTM和GRU的TimeGAN模型,选取性能更优的GRU网络作为TimeGAN模型的组成单元,然后采用Nadam优化算法对TimeGAN模型的各组件进行优化,即构建Nadam-TimeGAN模型用以数据扩充,最后构建一个平衡的数据集输入XGBoost集成学习模型进行分类训练.实验选取转辙机动作电流数据集进行验证性实验,选取MFPT轴承数据集和CWRU轴承数据集进行泛化性实验,并与8种方法进行对比,结果表明,所提方法在准确率、召回率以及F1-score这3种评价指标上均高于其他方法,从而验证了所提方法在不平衡数据故障诊断方面的有效性和泛化性.
Abstract
In order to improve the diagnostic performance and model generalization ability of the fault diagnosis model in data imbalance scenarios,a time series signal fault diagnosis method based on Nadam-TimeGAN and XGBoost was proposed.Firstly,the TimeGAN model based on LSTM and GRU was compared,and the GRU network with better per-formance was selected as the component unit of the TimeGAN model.The Nadam optimization algorithm was used to optimize the components of the TimeGAN model,that was,the Nadam-TimeGAN model was constructed for data expan-sion.After data expansion,a balanced data set was constructed and input into the XGBoost integrated learning model for classification training.In the experiment,the action current data set of switch machine was selected for verification ex-periment,the MFPT bearing data set and the CWRU bearing data set were selected for generalization experiment,and compared with eight methods.The results show that the proposed method is higher than other methods in accuracy,recall and F1-score.The experimental results validate the effectiveness and generalization of the proposed method for imbal-anced data fault diagnosis.
关键词
时间序列生成对抗网络/Nesterov加速自适应矩估计/极致梯度提升/故障诊断/数据增强Key words
TimeGAN/Nadam/XGBoost/fault diagnosis/data augmentation引用本文复制引用
基金项目
国家自然科学基金(62120106011)
国家自然科学基金(U20B2050)
国家重点研发计划(2022YFB2602203)
出版年
2024
国家科技期刊平台
NSTL
维普
万方数据