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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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-05-01
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| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/15/11/5933 |
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