Generative adversarial network-based fault diagnosis model for railway point machine in sustainable railway transportation

The railway point machine is a significant element of railway signalling equipment that keeps trains running safely. Railway point devices are actuators that move switchblades to reroute trains in different directions. The features are manually designed and used for dimension reduction in conventional methods. This research proposes a generative adversarial network-based fault diagnosis model in the railway point machine based on vibration signals for sustainable railway transportation. It is pre-trained on datasets; the test error can be forecasted without extra hyperparameter tuning in railway points. The system provides a method for obtaining Mel-frequency-cestrum-coefficients from audio information with fewer feature dimensions utilising a gradient-boosted decision tree and the generative adversarial network incorporating a discriminative model in its process for early prediction and classification of abnormalities in railway points. The numerical outcomes indicate that the suggested model enhances the classification accuracy ratio, precision ratio, recall ratio, and fault audio diagnosis performance ratio.