Intelligent algorithms to detect track geometry defects using vehicle acceleration

This paper presents an approach that aims to detect track geometry defects using machine learning techniques. It specifically involves the implementation of a bidirectional long short-term memory (LSTM) neural network to analyze displacement data derived from sensor acceleration signals. The methodology is validated using the standards set by the Rail Industry Safety and Standards Board (RISSB). By utilizing a dynamics vehicle model simulation, the approach extracts displacement data, which enables the training of the LSTM model. The neural network consists of three layers: a bidirectional LSTM layer, a fully connected layer, and an output layer that indicates the defect label for a given input. Through experimentation with various parameter configurations, the current implementation achieves a detection accuracy of approximately 92.7% within a 10-s time window. The proposed approach demonstrates the potential of machine learning in intelligently identifying track geometry defects, highlighting its practicality in the rail industry. By automating the defect detection process, this technique provides valuable insights for maintenance crews, facilitating timely repairs and enhancing safety and operational efficiency.