Article: Intelligent fault diagnosis system for railway infrastructure based on deep learning Journal: International Journal of Information and Communication Technology (IJICT) 2025 Vol.26 No.41 pp.107 - 122 Abstract: The operational status of railway infrastructure determines the safety of train passage. However, traditional research suffers from low efficiency and difficulty in addressing fault states under variable operating conditions. To address this, this paper first proposes a data balancing method based on improved synthetic minority over-sampling technique and generative adversarial network (GAN) to tackle the imbalance in railway infrastructure signal data. The introduction of unsupervised clustering algorithms and natural neighbour concepts enhances sample generation efficiency. Adding category label information and optimising the training loss function improves the stability of network training. Building upon this foundation, a multi-scale residual network (ResNet) is constructed for feature extraction, mitigating the impact of operational variations on diagnostic outcomes. A subdomain-adaptive transfer learning strategy is employed to achieve fault diagnosis. Experimental validation demonstrates that the proposed method achieves a diagnostic accuracy of 93.86%, delivering highly precise diagnostic results. Inderscience Publishers - linking academia, business and industry through research

Title: Intelligent fault diagnosis system for railway infrastructure based on deep learning

Authors: Qi An

Addresses: Signal and Communication Research Institute, China Academy of Railway Science Corporation Limited, Beijing 100081, China

Abstract: The operational status of railway infrastructure determines the safety of train passage. However, traditional research suffers from low efficiency and difficulty in addressing fault states under variable operating conditions. To address this, this paper first proposes a data balancing method based on improved synthetic minority over-sampling technique and generative adversarial network (GAN) to tackle the imbalance in railway infrastructure signal data. The introduction of unsupervised clustering algorithms and natural neighbour concepts enhances sample generation efficiency. Adding category label information and optimising the training loss function improves the stability of network training. Building upon this foundation, a multi-scale residual network (ResNet) is constructed for feature extraction, mitigating the impact of operational variations on diagnostic outcomes. A subdomain-adaptive transfer learning strategy is employed to achieve fault diagnosis. Experimental validation demonstrates that the proposed method achieves a diagnostic accuracy of 93.86%, delivering highly precise diagnostic results.

Keywords: railway infrastructure; fault diagnosis; synthetic minority over-sampling technique; generative adversarial network; GAN; transfer learning.

DOI: 10.1504/IJICT.2025.149991

International Journal of Information and Communication Technology, 2025 Vol.26 No.41, pp.107 - 122

Received: 31 Aug 2025
Accepted: 28 Sep 2025
Published online: 20 Nov 2025 *

Title: Intelligent fault diagnosis system for railway infrastructure based on deep learning

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