High-precision distributed detection of rail defects by tracking the acoustic propagation waves.

Nowadays, early defect detection plays a significant role for the railway safety warning. However, the existing methods cannot satisfy the requirements of real-time and high-precision detection. Here, a high-precision, distributed and on-line method for detecting rail defect is proposed and demonstrated. When a train goes through defects, the instantaneous elastic waves will be excited by the wheel-rail interaction, which will further propagate along railway tracks bidirectionally. Through mounting the backscattering enhanced optical fiber on the railway as sensors, the fiber optic distributed acoustic sensing system can record the propagation trace precisely. Further, the acoustic propagation fitting method is applied onto the propagation data to detect and locate defects along the long-distance railway. Especially, the dual-frequency joint-processing algorithm is proposed to improve the location accuracy. The field test proves that multiple defects along the railway can be successfully identified and located with a standard deviation of 0.314m. To the best of our knowledge, this work is the first report of distributed rail defect detection, which will bring a breakthrough for high-precision structural damage detection in the infrastructures such as the railway, pipeline and tunnel.

Spatio-temporal joint oversampling-downsampling technique for ultra-high resolution fiber optic distributed acoustic sensing.

In order to suppress the noise of the coherent fiber distributed acoustic sensing (DAS) system, the spatio-temporal joint oversampling-downsampling technique is proposed. The spatial oversampling is used for artificially dense sampling, whose spacing is far less than the target spatial resolution. Then the spatial downsampling performed by the average of multiple differential sub-vectors is utilized to reduce the influence of noise vectors, which could completely eliminate the interfere fading without increasing any system complexity and introducing any crosstalk. Meanwhile, the temporal oversampling-downsampling is analyzed from the perspective of theory and simulation, demonstrating that the noise floor will decrease with the increase of downsampling coefficient. The temporal oversampling is carried out to expand the noise distribution bandwidth and ensure the correct quantization of the noise frequency. Then the temporal downsampling of differential phase reconstruction is utilized to recover the target bandwidth and reduce the out-of-band noise. The experimental results prove that the noise floor is inversely correlated with the spatiotemporal downsampling factors. The strain resolution of the DAS system with the proposed scheme can reach 2.58pε/√Hz@100Hz-500Hz and 9.47pε/√Hz@10Hz under the condition of DC-500Hz target bandwidth, as well as the probability of the large-noise sensing channels is greatly reduced from 44.32% to 0%. Moreover, the demodulated SNR of dynamic signal is improved by 20.8dB compared with the traditional method. Without any crosstalk, the noise floor is optimized 8dB lower than the averaging technique. Based on the proposed method, the high-performance DAS system has significant competitiveness in the applications with the demand of high-precision and high-sensitivity, such as passive-source seismic imaging and VSP exploration.