Acoustic Monitoring of Railway Defects Using Deep Learning with Audio to Spectrogram Conversion
Author:
Funder
Süleyman Demirel Üniversitesi
Türkiye Bilimsel ve Teknolojik Araştırma Kurumu
Yükseköğretim Kurulu
Publisher
Springer Science and Business Media LLC
Subject
Microbiology (medical),Immunology,Immunology and Allergy
Link
https://link.springer.com/content/pdf/10.1007/s42417-023-01001-8.pdf
Reference47 articles.
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2. Ngamkhanong C, Kaewunruen S, Afonso Costa BJ (2018) State of the art review of railway track resilience monitoring. Infrastruct 3(1):3. https://doi.org/10.3390/infrastructures3010003
3. Mair C, Fararooy S (1998) Practice and potential of computer vision for railways. IEE seminar condition monitoring for rail transport systems. IEEE, London, pp 10–13. https://doi.org/10.1049/ic:19980983
4. Marais JJ, Mistry KC (2003) Rail integrity management by means of ultrasonic testing. Fatigue Fract Eng Mater Struct 26(10):931–938. https://doi.org/10.1046/j.1460-2695.2003.00668.x
5. Fitzgerald SC (1995) Inspection for rail defects by magnetic induction. Non-destructive evaluation of aging railroads. SPIE, Oakland, pp 40–44. https://doi.org/10.1117/12.212672
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