Open AccessA Modified Generative Adversarial Network for Fault Diagnosis in High-Speed Train Components with Imbalanced and Heterogeneous Monitoring Data
Author(s) -
Chong Wang,
Jie Liu,
Enrico Zio
Publication year - 2022
Publication title -
journal of dynamics, monitoring and diagnostics
Language(s) - English
Resource type - Journals
eISSN - 2833-650X
pISSN - 2831-5308
DOI - 10.37965/jdmd.2022.68
Subject(s) - discriminator , categorical variable , computer science , robustness (evolution) , data mining , fault (geology) , generative grammar , generative adversarial network , adversarial system , artificial intelligence , machine learning , deep learning , telecommunications , biochemistry , chemistry , detector , seismology , gene , geology
Data-driven methods are widely considered for fault diagnosis in complex systems. However, in practice the between-class imbalance due to limited faulty samples may deteriorate their classification performance. To address this issue, synthetic minority methods for enhancing data have been proved to be effective in many applications. Generative Adversarial Networks (GANs), capable of automatic features extraction, can also be adopted for augmenting the faulty samples. However, the monitoring data of a complex system may include not only continuous signals, but also discrete/categorical signals. Since the current GAN methods still have some challenges in handling such heterogeneous monitoring data, a Mixed Dual Discriminator GAN (noted as M-D2GAN) is proposed in this work. In order to render the expanded fault samples more aligned with the real situation, and improve the accuracy and robustness of the fault diagnosis model, different types of variables are generated in different ways, including: floating-point, integer, categorical, and hierarchical. For effectively considering the class imbalance problem, proper modifications are made to the GAN model, where a normal class discriminator is added. A practical case study concerning the braking system of a high-speed train is carried out to verify the effectiveness of the proposed framework. Compared to the classic GAN, the proposed framework achieves better results with respect to F-measure and G-mean metrics.