Railroad condition monitoring with distributed acoustic sensing: an investigation of deep learning methods for condition detection

Abstract

Distributed acoustic sensing (DAS) using fiber optic cables over an extensive length of railroads is a well-suited technique for condition monitoring (CM) of railroads. Regardless of the type of indication in railroad CM, the original large and noisy dataset from the DAS system is a major challenge in DAS data analysis. Different data analysis strategies, such as conventional peak finding or neural networks, can be considered for DAS data analysis depending on the purpose of the study and characteristics of the railroad. We aim to investigate the robustness of deep learning (DL) models based on long-shot-term memory (LSTM) and gated recurrent unit (GRU) approaches. The average trend of the recorded technical data management streaming signals was used to extract the train presence or absence conditions along the railroad. This investigation showed that DL approaches could be efficient for DAS signal processing and CM in railroad infrastructures and can be expanded in the future for other CM purposes such as flaw detection. Meanwhile, for train position monitoring, the proposed model based on the GRU architecture indicated a 94% detection rate compared with 93% by the LSTM model. In all, the proposed models show promising potential for efficiently detecting railroad conditions, such as anomalies and flaws that require further investigation.