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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.
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This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
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