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Improved railway track faults detection using Mel-frequency cepstral coefficient and constant-Q transform features
Rahman Shafique1, Khadija Kanwal2, Venkata Chunduri3
1Department of Information and Communication Engineering, Yeungnam University, Gyeongsan, 38541, Republic of Korea.
Scientific Reports
|August 22, 2025
Summary
Automated railway track fault detection using acoustic analysis offers a 100% accurate method for identifying defects. This technique enhances train safety by enabling early detection of issues like cracks and misalignment.
Area of Science:
- Engineering
- Data Science
- Transportation Safety
Background:
- Regular railway track inspection is vital for safe train operations.
- Manual inspection methods are inefficient and prone to human error, leading to potential accidents.
- Automating fault detection is crucial, especially in regions like Pakistan with reported train accidents.
Purpose of the Study:
- To enhance railway track fault detection using an automated acoustic analysis technique.
- To address the challenge of limited data by employing the CTGAN technique for dataset augmentation.
- To improve the safety and reliability of railway transportation through advanced detection methods.
Main Methods:
- Utilized acoustic data for railway track fault detection.
- Employed the CTGAN (Conditional Tabular Generative Adversarial Network) technique to enlarge the dataset.
- Applied logistic regression for the classification of railway track faults.
Main Results:
- Acoustic data proved effective in identifying railway track faults.
- The CTGAN technique successfully augmented the dataset.
- Logistic regression achieved 100% accuracy in classifying railway track faults.
Conclusions:
- Acoustic analysis is a viable and highly accurate method for automated railway track fault detection.
- Data augmentation techniques like CTGAN can significantly support the development of robust detection systems.
- The proposed automated system offers a promising solution for enhancing railway safety and preventing accidents.
Keywords:
Acoustic dataConstant-Q transform featuresFault detectionMachine learningMel-Frequency Cepstral CoefficientRailway track monitoringMore Related Videos
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