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

doi:10.1007/s40436-024-00496-y

Advances in Manufacturing ›› 2024, Vol. 12 ›› Issue (3): 603-618.doi: 10.1007/s40436-024-00496-y

• • 上一篇

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

Wen-Jie Wang^1,2, Zhao Liu³, Ping Zhu^1,2

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

收稿日期:2023-09-27 修回日期:2023-11-06 发布日期:2024-09-07
通讯作者: Zhao Liu,E-mail:hotlz@sjtu.edu.cn;Ping Zhu,E-mail:pzhu@sjtu.edu.cn E-mail:hotlz@sjtu.edu.cn;pzhu@sjtu.edu.cn
作者简介:Wen-Jie Wang received his B.S. degree in Mechanical Engineering from Mao Yisheng Honors College at Southwest Jiaotong University. He is currently a Ph.D. candidate in the School of Mechanical Engineering at Shanghai Jiao Tong University. His current research interests include deep learning and data mining techniques, industrial big data analysis, and data-driven automotive crash safety performance prediction;
Zhao Liu is currently an associate professor in the School of Design, Shanghai Jiao Tong University. He received his Ph.D. from the School of Mechanical Engineering at Shanghai Jiao Tong University in 2016. His research interests include intelligent optimization theory, intelligent design theory, data-driven design, machine learning, and data mining. He has published more than 40 papers in international journals and conference proceedings, and has obtained more than 20 Chinese national invention patents;
Ping Zhu is currently a tenured professor in the School of Mechanical Engineering at Shanghai Jiao Tong University. He received his Ph.D. in Mechanical Engineering from Miyazaki University in 2000. He is the deputy director of the Automotive Safety Division of SAE-China and Automotive Reliability Committee of SAEShanghai, a senior member of the Chinese Mechanical Engineering Society (CMES), and an associate editor of the Journal of Mechanical Design (JMD) of the ASME. His research interests include lightweight design and manufacturing, automotive safety and reliability, big data analysis and digital twins, data-driven metamaterial design and optimization, and integrated material-structure-process-performance design. In recent years, he has published more than 320 papers in domestic and international journals, and more than 30 Chinese national invention patents.
基金资助:
This work was supported by the National Natural Science Foundation of China (Grant No. 52375256) and the Natural Science Foundation of Shanghai Municipality (Grant Nos. 21ZR1431500 and 23ZR1431600).

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

Wen-Jie Wang^1,2, Zhao Liu³, Ping Zhu^1,2

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

Received:2023-09-27 Revised:2023-11-06 Published:2024-09-07
Contact: Zhao Liu,E-mail:hotlz@sjtu.edu.cn;Ping Zhu,E-mail:pzhu@sjtu.edu.cn E-mail:hotlz@sjtu.edu.cn;pzhu@sjtu.edu.cn
Supported by:
This work was supported by the National Natural Science Foundation of China (Grant No. 52375256) and the Natural Science Foundation of Shanghai Municipality (Grant Nos. 21ZR1431500 and 23ZR1431600).

摘要/Abstract

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.

The full text can be downloaded at https://link.springer.com/article/10.1007/s40436-024-00496-y

关键词: Class imbalance, Minority class augmentation, Generative adversarial network (GAN), Boundary strengthening learning (BSL), Fault prediction

Abstract: 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.

The full text can be downloaded at https://link.springer.com/article/10.1007/s40436-024-00496-y

Key words: Class imbalance, Minority class augmentation, Generative adversarial network (GAN), Boundary strengthening learning (BSL), Fault prediction

Wen-Jie Wang, Zhao Liu, Ping Zhu. CMAGAN: classifier-aided minority augmentation generative adversarial networks for industrial imbalanced data and its application to fault prediction[J]. Advances in Manufacturing, 2024, 12(3): 603-618.

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

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

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