Improved railway track faults detection using Mel-frequency cepstral coefficient and constant-Q transform features.

Regular inspection of the health of railway tracks is crucial to maintaining reliable and safe train operations. Some factors including cracks, rail discontinuity, ballast issues, burn wheels, super-elevation, loose nuts and bolts, and misalignment developed on the railways due to pre-emptive investigations, non-maintenance, and delay in detection pose grave threats and danger to the safe operation of railway transportation. In the past, manual inspection was performed for the rail track by a rail cart which is both prone to error and inefficient due to human biases and error. Several train accidents are reported in Pakistan; it is important to automate these techniques to avoid such train accidents for the safety of countless lives. This study aims to enhance railway track fault detection using an automatic rail track fault detection technique with acoustic analysis. Moreover, the proposed method contributes to making the dataset large by using the CTGAN technique. Results show that acoustic data may help to determine the railway track faults effectively and logistic regression is used to perform the classification for railway track faults with an accuracy of 100%.