An Effective Method for Imbalanced Sample Generation and Its Application in Fault Diagnosis of Planetary Gearbox

doi:10.3969/j.issn.1000-1093.2019.07.003

Abstract

Abstract: A fault diagnosis classification model based on WGAN-CNN is proposed for few fault data of planetary gearbox and the imbalanced samples of each state in actual operation. The proposed model is a combination of Wasserstein generative adversarial network (WGAN), a sample generation model, a convolutional neural network (CNN), and a sample classification model. The model is used to oversample the spectral signals of fault data and expand the number of fault samples, thus classifying the fault states better. UCI artificial datasets were used to compare WGAN generation model and classical oversampling methods, and verified on a planetary gearbox fault test rig. The results show that the imbalanced samples seriously affect the classification results, and the WGAN-CNN model can well expand the fault sample datasets and improve the diagnostic accuracy in the case of rare fault samples. Key

Key words: planetarygearbox, imbalancesamples, Wassersteingenerativeadversarialnetwork, convolutionalneuralnetwork

CLC Number:

WU Chunzhi, FENG Fuzhou, WU Shoujun, CHEN Tang, JIANG Pengcheng. An Effective Method for Imbalanced Sample Generation and Its Application in Fault Diagnosis of Planetary Gearbox[J]. Acta Armamentarii, 2019, 40(7): 1349-1357.

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第40卷
第7期2019 年7月兵工学报ACTA
ARMAMENTARIIVol.40No.7Jul.2019

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