Diesel Engine Fault Diagnosis Method Based on Fusion Data Augmentation
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.
data augmentationfault diagnosisdiesel enginegenerative adversarial network(GAN)
国家科技期刊平台
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