An Unsupervised Anomaly Detection Method for Railway Fasteners Based on Knowledge-Distilled Generative Adversarial Networks
The integrity and stability of railway fasteners are of vital importance to railway safety. To address the challenges of limited anomaly samples, irregular defect geometries, and complex operational conditions in rail fastener anomaly detection, this paper proposes an unsupervised anomaly detection...
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2025-05-01
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| author | Hongyan Chen Zhiwei Li Xinjie Xiao |
| author_facet | Hongyan Chen Zhiwei Li Xinjie Xiao |
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| description | The integrity and stability of railway fasteners are of vital importance to railway safety. To address the challenges of limited anomaly samples, irregular defect geometries, and complex operational conditions in rail fastener anomaly detection, this paper proposes an unsupervised anomaly detection method using a knowledge-distilled generative adversarial network. First, the proposed method employs collaborative teacher–student learning to model normal sample distributions, where the student network reconstructs input images as normal outputs while a discriminator identifies anomalies by comparing input and reconstructed images. Second, a multi-scale attention-coupling feature-enhancement mechanism is proposed, effectively integrating hierarchical semantic information with spatial-channel attention to achieve both precise target localization and robust background suppression in the teacher network. Third, an enhanced anomaly discriminator is designed to incorporate an enhanced pyramid upsampling module, through which fine-grained details are preserved via multi-level feature map aggregation, resulting in significantly improved sensitivity for small-sized anomaly detection. Finally, the proposed method achieved an AUC of 94.0%, an ACC of 92.5%, and an F1 score of 91.6% on the MNIST dataset, and an AUC of 94.7%, an ACC of 90.1%, and an F1 score of 87.8% on the railway fastener dataset, which proves the superior anomaly detection ability of this method. |
| format | Article |
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| issn | 2076-3417 |
| language | English |
| publishDate | 2025-05-01 |
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| spelling | doaj-art-721fbefabddf40ff947292faf83ec7882025-08-20T02:32:34ZengMDPI AGApplied Sciences2076-34172025-05-011511593310.3390/app15115933An Unsupervised Anomaly Detection Method for Railway Fasteners Based on Knowledge-Distilled Generative Adversarial NetworksHongyan Chen0Zhiwei Li1Xinjie Xiao2School of Electronic and Electrical Engineering, Shanghai University of Engineering Science, Shanghai 201620, ChinaSchool of Electronic and Electrical Engineering, Shanghai University of Engineering Science, Shanghai 201620, ChinaCollege of Information Science and Technology, Donghua University, Shanghai 201620, ChinaThe integrity and stability of railway fasteners are of vital importance to railway safety. To address the challenges of limited anomaly samples, irregular defect geometries, and complex operational conditions in rail fastener anomaly detection, this paper proposes an unsupervised anomaly detection method using a knowledge-distilled generative adversarial network. First, the proposed method employs collaborative teacher–student learning to model normal sample distributions, where the student network reconstructs input images as normal outputs while a discriminator identifies anomalies by comparing input and reconstructed images. Second, a multi-scale attention-coupling feature-enhancement mechanism is proposed, effectively integrating hierarchical semantic information with spatial-channel attention to achieve both precise target localization and robust background suppression in the teacher network. Third, an enhanced anomaly discriminator is designed to incorporate an enhanced pyramid upsampling module, through which fine-grained details are preserved via multi-level feature map aggregation, resulting in significantly improved sensitivity for small-sized anomaly detection. Finally, the proposed method achieved an AUC of 94.0%, an ACC of 92.5%, and an F1 score of 91.6% on the MNIST dataset, and an AUC of 94.7%, an ACC of 90.1%, and an F1 score of 87.8% on the railway fastener dataset, which proves the superior anomaly detection ability of this method.https://www.mdpi.com/2076-3417/15/11/5933rail fasteneranomaly detectionknowledge-distilled generative adversarial networkmulti-scale attention couplingmulti-level feature map aggregation |
| spellingShingle | Hongyan Chen Zhiwei Li Xinjie Xiao An Unsupervised Anomaly Detection Method for Railway Fasteners Based on Knowledge-Distilled Generative Adversarial Networks Applied Sciences rail fastener anomaly detection knowledge-distilled generative adversarial network multi-scale attention coupling multi-level feature map aggregation |
| title | An Unsupervised Anomaly Detection Method for Railway Fasteners Based on Knowledge-Distilled Generative Adversarial Networks |
| title_full | An Unsupervised Anomaly Detection Method for Railway Fasteners Based on Knowledge-Distilled Generative Adversarial Networks |
| title_fullStr | An Unsupervised Anomaly Detection Method for Railway Fasteners Based on Knowledge-Distilled Generative Adversarial Networks |
| title_full_unstemmed | An Unsupervised Anomaly Detection Method for Railway Fasteners Based on Knowledge-Distilled Generative Adversarial Networks |
| title_short | An Unsupervised Anomaly Detection Method for Railway Fasteners Based on Knowledge-Distilled Generative Adversarial Networks |
| title_sort | unsupervised anomaly detection method for railway fasteners based on knowledge distilled generative adversarial networks |
| topic | rail fastener anomaly detection knowledge-distilled generative adversarial network multi-scale attention coupling multi-level feature map aggregation |
| url | https://www.mdpi.com/2076-3417/15/11/5933 |
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