Railway Track Online Detection Based on Optical Fiber Distributed Large-Range Acoustic Sensing

An optical fiber distributed acoustic sensing system for large infrastructure vibration monitoring is proposed in this work. To meet the requirements of measurement range, spatial resolution, and real-time performance of the monitoring network, the range extension algorithm is proposed to optimize the recovery of large signals for the distributed acoustic sensing monitoring of large-scale infrastructure structure monitoring networks. Furthermore, the technology is applied to heavy rail track defect detection, where existing track-side communication cables are used to directly monitored vibration signals with the distributed acoustic sensing system. Multiple characteristic parameters are combined to form a multi-dimensional eigenvector, and then combined with the machine learning algorithm to enable the recognition of typical track defects along the heavy-haul railway. The experimental results demonstrate that the recognition and classification of typical track defects such as rolling contact fatigue, corrugation, and unsupported sleepers. The real-time detection of track defects in this work can be used as a crucial basis for workers to maintain and repair the railway. Finally, a long-term real-time online monitoring method is proposed in this work for vibration monitoring of large-scale infrastructures with large-amplitude/low-SNR signals using existing track-side communication cables, without any additional sensor arrangement.