A Novel Event Detection Method for OTDR Trace with High Sensitivity Based on Machine Learning

Optical Time-Domain Reflectometer (OTDR) is the most common way for quality evaluation and fault location of optical fibers. Aiming at the problem of low detection rate caused by low sensitivity in traditional OTDR event detection methods, a novel detection method based on machine learning is proposed. Firstly, the OTDR signal is processed with n-order difference, then the differential signal is denoised. In order to reduce the computation complexity, the peaks of the differential signal are found and the features of the peaks are extracted. Finally, the features are labeled and sent to a machine learning based classifier for offline training. The trained model is used for online prediction to output the detected events. The algorithm is verified with 500 OTDR trace, the results show that the detection rate of connection splice events is up to 95% after 200 iterations. The sensitivity is much higher than that of the world-famous OTDR instrument.