内燃机工程2024，Vol.45Issue(6)：71-80,10.DOI:10.13949/j.cnki.nrjgc.2024.06.008
基于融合数据增强的柴油机故障诊断方法
Diesel Engine Fault Diagnosis Method Based on Fusion Data Augmentation
摘要
Abstract
Aiming at the problem that the diesel engine fault diagnosis method based on deep learning overfitting occured and led to a decrease in diagnosis accuracy when the number of training samples was scarce,an diesel engine fault diagnosis method based on fusion data augmentation was proposed.The fault samples in the training set were expanded using fusion data augmentation methods.The synthetic minority over-sampling technology(SMOTE)and auxiliary classifier generative adversarial network(ACGAN)were combined to generate samples in two stages.K-nearest neighbors(KNN)was used to remove the noise generating samples.Then the expanded training set was used to train a deep learning fault diagnosis model for recognizing unknown vibration signals.The measured signals of the diesel engine fault simulation experiment show that the one-dimensional convolutional neural network(1DCNN)can achieve a fault diagnosis accuracy of 90.21%with only 10 samples used for model training and the proposed fusion data augmentation method can improve the fault diagnosis accuracies of different deep learning models.关键词
数据增强/故障诊断/柴油机/生成对抗网络Key words
data augmentation/fault diagnosis/diesel engine/generative adversarial network(GAN)分类
能源科技引用本文复制引用
景亚兵,郭明智,毕晓阳..基于融合数据增强的柴油机故障诊断方法[J].内燃机工程,2024,45(6):71-80,10.基金项目
河北省高等学校科学技术研究项目(QN2022159)Science and Technology Research Project of Higher Education in Hebei Province(QN2022159) （QN2022159）
