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This paper investigates the influences of laser source on distributed intrusion sensor based on a phase-sensitivity optical time-domain reflectometer (φ-OTDR). A numerical simulation is performed to illustrate the relationships between trace-to-trace fluctuations and frequency drift rate as well as pulse width, and fluctuations ratio coefficient (FRC) is proposed to evaluate the level of trace-to-trace fluctuations. The simulation results show that the FRC grows with increasing frequency drift rate and pulse width, reaches, and maintains the peak value when the frequency drift rate and/or the pulse width are high enough. Furthermore, experiments are implemented using a φ-OTDR prototype with a low frequency drift laser (<5  MHz/min), of which the high frequency drift rate is simulated by frequency sweeping. The good agreement of experimental with simulated results in the region of high frequency drift rate validates the theoretical analysis, and the huge differences between them in the region of low frequency drift rate indicate the place of laser frequency drift among system noises. The conclusion is useful for choosing laser sources and improving the performance of φ-OTDR.

A fiber-optic intrinsic distributed acoustic emission (AE) sensor is proposed. By measuring the time delay of two signals from two Mach-Zehnder interferometers, the location of AE can be deduced, and the corresponding sensor is experimentally verified to be feasible with a 206 m average location error in a 20 km sensing range, which shows that this proposed sensor is applicable for distributed AE sensing for large structure health monitoring, with the unique advantages of low cost, simple configuration, and long sensing range. The limitations of the proposed sensor are also discussed, and the future work is presented.

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A novel pattern recognition method based on Empirical Mode Decomposition (EMD) and extreme gradient boosting (XGBoost) is proposed to recognize the disturbance events in phase sensitive optical time-domain reflectometer (ϕ-OTDR) to reduce nuisance alarm rate (NAR) and improve real-time performance in this paper. Eleven typical eigenvectors are extracted from components obtained by EMD of the disturbance signals and XGBoost is selected as a classifier to identify different type of disturbance signals. Five kinds of disturbance events, including watering, knocking, climbing, pressing and false disturbance event, can be identified, effectively. Experimental results show that NAR is 4.10% and identification time is 0.093 s. The recognition accuracy for the five patterns is 97.96%, 95.90%, 91.10%, 94.84% and 99.69%, respectively. The effectiveness of the proposed method is evaluated by using confusion matrix and decision boundary visualization. Experimental results demonstrate that our proposed pattern recognition method based on XGBoost has better performance in recognition rate and recognition time than other commonly used methods, such as support vector machine (SVM), Gradient Boosting Decision Tree (GBDT), Random Forest (RF) and Adaptive Boosting (Adaboost). INDEX TERMS Phase-sensitive optical time-domain reflectometer (ϕ-OTDR), extreme gradient boosting (XGBoost), nuisance alarm rate (NAR), empirical mode decomposition (EMD), pattern recognition, decision boundary visualization.

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