A review of distributed acoustic sensing applications for railroad condition monitoring

Accurate condition monitoring has been a major challenge among railroad management authorities as they work to minimize collisions that lead to fatalities or damage to railroads infrastructure. Hence, research and technological developments in railroad maintenance and inspection are vital. Several research studies, on the inspection and defect detection techniques for railroads have been conducted by scholars. Despite the significant advancements made in this area, extensive studies are still required to enhance the accuracy of prognostic methods for railroad structural health monitoring (SHM) and condition monitoring (CM). Distributed acoustic sensing (DAS) has been recognized as a promising measurement method because of its quick sensing capabilities over long distances and for massive structures. DAS systems are classified according to the optical sensing quality and sensing range. As DAS produces large noisy datasets, in the case of railroad applications, algorithms for precise real-time and reliable analysis are essential. Meanwhile, data-driven and machine learning (ML) methods for defect detection have emerged as valuable approaches for SHM. Engineers and stakeholders can use ML algorithms to examine the large volumes of data produced by SHM systems to identify patterns and behaviors that might not appear in manual inspections. To support more precise and accurate maintenance and inspection for railroad systems, methodologies used to detect, identify, and characterize abnormal conditions in railroads using DAS and signal processing approaches for DAS large size and noisy signals, must be reviewed. Accordingly, in this literature survey, the applications of DAS methods for railroad CM are investigated. Among the variety of DAS methods, optical time domain reflectometry (OTDR) is reviewed in more details, since it is the most common approach in distributed fiber optic sensing. In addition, different OTDR-based DAS research for train tracking and railroad SHM are reviewed, and a comprehensive summary of different railroad defects is provided for further investigation. In all, this review paper provides a comprehensive background on distributed fiber optic sensing, the importance and challenges in railroad continuous CM, and the state-of-the-art application and future roadmap for the application of DAS in the railroad industry.