Discover the SciOpen Platform and Achieve Your Research Goals with Ease.
Search articles, authors, keywords, DOl and etc.
In view of class imbalance in data-driven modeling for Prognostics and Health Management (PHM), existing classification methods may fail in generating effective fault prediction models for the on-board high-speed train control equipment. A virtual sample generation solution based on Generative Adversarial Network (GAN) is proposed to overcome this shortcoming. Aiming at augmenting the sample classes with the imbalanced data problem, the GAN-based virtual sample generation strategy is embedded into the establishment of fault prediction models. Under the PHM framework of the on-board train control system, the virtual sample generation principle and the detailed procedures are presented. With the enhanced class-balancing mechanism and the designed sample augmentation logic, the PHM scheme of the on-board train control equipment has powerful data condition adaptability and can effectively predict the fault probability and life cycle status. Practical data from a specific type of on-board train control system is employed for the validation of the presented solution. The comparative results indicate that GAN-based sample augmentation is capable of achieving a desirable sample balancing level and enhancing the performance of correspondingly derived fault prediction models for the Condition-based Maintenance (CBM) operations.
B. Lin, Y. Shen, Z. Wang, et al., An iterative improvement approach for high-speed train maintenance scheduling, Transp. Res. Part B: Methodol. 173 (2023) 292-312.
B. Lin, J. Wu, R. Lin, et al., Optimization of high-level preventive maintenance scheduling for high-speed trains, Reliab. Eng. Syst. Saf. 183 (2019) 261-275.
M. Biagi, L. Carnevali, M. Paolieri, et al., Performability evaluation of the ERTMS/ETCS–Level 3, Transp. Res. Part C: Emerg. Technol. 82 (2017) 314-336.
B. Ning, T. Tang, K. Qiu, et al., CTCS - Chinese train control system, WIT Trans. Built Environ. 74 (2004).
S. Ochella, M. Shafiee, F. Dinmohammadi, Artificial intelligence in prognostics and health management of engineering systems, Eng. Appl. Artif. Intell. 108 (2022) 104552.
K. Zhong, J. Wang, S. Xu, et al., Overview of fault prognosis for traction systems in high-speed trains: A deep learning perspective, Eng. Appl. Artif. Intell. 126 (2023) 106845.
E. Zio, Prognostics and Health Management (PHM): where are we and where do we (need to) go in theory and practice, Reliab. Eng. Syst. Saf. 218 (2022) 108119.
Q. Xu, P. Zhang, W. Liu, et al., A platform for fault diagnosis of high-speed train based on big data, IFAC-Pap. 51 (18) (2018) 309-314.
J. Guerreiro, P. Tomás, N. Garcia, et al., Super-resolution of magnetic resonance images using generative adversarial networks, Comput. Med. Imaging Graph. 108 (2023) 102280.
V. Souza, B. Marques, H. Batagelo, et al., A review on generative adversarial networks for image generation, Comput. Graph. 114 (2023) 1-12.
I. Goodfellow, J. Pouget-Abadie, M. Mirza, et al., Generative adversarial nets, Proceedings of the 27th International Conference on Neural Information Processing Systems 2 (2014) 2672-2680.
M. Contreras-Cruz, F. Correa-Tome, R. Lopez-Padilla, et al., Generative adversarial networks for anomaly detection in aerial images, Comput. Electr. Eng. 106 (2023) 108470.
J. Friedman, T. Hastie, R. Tibshirani, Additive logistic regression: a statistical view of boosting (with discussion and a rejoinder by the authors), Ann. Stat. 28 (2) (2000) 337-407.
T. Chen, C. Guestrin, XGBoost: a scalable tree boosting system, Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2016) 785-794.
Y. Zhang, X. Shi, S. Zhang, et al., A XGBoost-based lane change prediction on time series data using feature engineering for autopilot vehicles, IEEE Trans. Intell. Transp. Syst. 23 (10) (2022) 19187-19200.
This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).