CMAGAN:classifier-aided minority augmentation generative adversarial networks for industrial imbalanced data and its application to fault prediction  

作　　者：Wen-Jie Wang Zhao Liu Ping Zhu 

机构地区：[1]School of Mechanical Engineering,Shanghai Jiao Tong University,Shanghai,200240,People’s Republic of China [2]National Engineering Research Center of Automotive Power and Intelligent Control,Shanghai Jiao Tong University,Shanghai,200240,People’s Republic of China [3]School of Design,Shanghai Jiao Tong University,Shanghai,200240,People’s Republic of China

出　　处：《Advances in Manufacturing》2024年第3期603-618,共16页先进制造进展（英文版）

基　　金：supported by the National Natural Science Foundation of China(Grant No.52375256);the Natural Science Foundation of Shanghai Municipality(Grant Nos.21ZR1431500 and 23ZR1431600).

摘　　要：Class imbalance is a common characteristic of industrial data that adversely affects industrial data mining because it leads to the biased training of machine learning models.To address this issue,the augmentation of samples in minority classes based on generative adversarial networks(GANs)has been demonstrated as an effective approach.This study proposes a novel GAN-based minority class augmentation approach named classifier-aided minority augmentation generative adversarial network(CMAGAN).In the CMAGAN framework,an outlier elimination strategy is first applied to each class to minimize the negative impacts of outliers.Subsequently,a newly designed boundary-strengthening learning GAN(BSLGAN)is employed to generate additional samples for minority classes.By incorporating a supplementary classifier and innovative training mechanisms,the BSLGAN focuses on learning the distribution of samples near classification boundaries.Consequently,it can fully capture the characteristics of the target class and generate highly realistic samples with clear boundaries.Finally,the new samples are filtered based on the Mahalanobis distance to ensure that they are within the desired distribution.To evaluate the effectiveness of the proposed approach,CMAGAN was used to solve the class imbalance problem in eight real-world fault-prediction applications.The performance of CMAGAN was compared with that of seven other algorithms,including state-of-the-art GAN-based methods,and the results indicated that CMAGAN could provide higher-quality augmented results.

关 键 词：Class imbalance Minority class augmentation Generative adversarial network(GAN) Boundary strengthening learning(BSL) Fault prediction 

分 类 号：O17[理学—数学]