Phase-sensitive optical time-domain reflection (Φ-OTDR) has the advantages of high accuracy, fast response speed, long monitoring distance, and anti-electromagnetic interference and has been widely used in dynamic sensing fields such as perimeter security and railway and pipeline monitoring. For direct detection intensity-demodulation Φ-OTDR, the pulse power is limited by the nonlinear effect, which causes a weak signal-to-noise ratio of the end signal, and its sensing distance is usually less than 25 km. Because the optical phase signal is linearly related to the vibration signal imposed on the fiber and coherent detection can significantly improve the detection sensitivity, the long-distance Φ-OTDR system mainly uses coherent detection and phase demodulation technology. Most coherent detection phase-demodulation Φ-OTDR system model recognition algorithms use phase signal as the input, combined with time-frequency feature extraction methods, such as Fourier transform and wavelet transform. However, interference fading occurs in the coherent detection system, which causes serious deterioration of the intensity signal, resulting in phase demodulation errors and false alarms. Common methods to eliminate interference fading are the frequency diversity, chirped pulses, and other frequency domain regulation technologies, which lead to complex system hardware. Moreover, owing to the variety of the disturbance signals and long sensing distance that results in a low signal-to-noise ratio of the end signal, Φ-OTDR systems suffer from false alarms in practical applications. It is of great significance to further improve the accuracy of the vibration signal identification for the timely detection of abnormal events.
A pattern recognition method based on a coherent detection Φ-OTDR system with mixed intensity and phase signal inputs is proposed, which can effectively reduce the impact of interference fading on the accuracy of event alarms without increasing the hardware complexity. The proposed method uses a hybrid deep neural network (HDNN), which combines a one-dimensional convolutional neural network (1DCNN) and a multi-layer perceptron (MLP), as shown in Fig. 4. The phase and intensity signal vectors are recovered simultaneously using the Hilbert demodulation algorithm. The phase and intensity vectors within a second are simply normalized by the max-min and tanh functions separately and then fed into the model. The model uses MLP to extract the fading noise features of the intensity signal and uses the 1DCNN model as the basic model to extract the disturbance characteristics of the phase signal. After the fusion of two-dimensional features and a classification layer, the model outputs the final detection results.
A long-distance Φ-OTDR system of more than 25 km was built. An adjustable optical attenuator (VOA) was used to simulate disturbance events occurring at different locations along the fiber, with attenuation of the VOA ranging from 1 dB to 7 dB. Four types of events, such as human beatings, walking, jumping, and machine excavating, are imposed at the outdoor optical cable buried 0.5 m underground. A 1DCNN network with only phase signal input was used as the comparison model. After multiple rounds of training, the experimental results show that the proposed HDNN model with intensity and phase signal inputs can achieve an average accuracy of 98.8%, which is better than the 1DCNN model result of 96.1% with only the phase signal input. Furthermore, comparing the confusion matrix of the two models, the 1DCNN model had the worst recognition accuracy of 91.0% with background noise and human beat events. In contrast, the HDNN model significantly improves the recognition accuracy of the two events to 99.4%. This shows that the interference fading anomalies contained in the background noise can be identified by the HDNN model with additional intensity input. For the other three types of events, the accuracy results of the two models are very close, indicating that the phase signal has a better ability to recover the vibration events than the intensity signal, which is consistent with the previous analysis.
Aiming to further improve the event alarm accuracy of the long-distance coherent detection Φ-OTDR system, a pattern recognition method with a mixed input of intensity and phase signals was proposed. To verify the improvement of the proposed method, a 1DCNN network with only the phase signal input was used as the comparison model. A hybrid deep neural network, combining 1DCNN and MLP, was used for the intensity and phase signal mixed-input classification. The model used MLP to extract the fading noise features of the intensity signal and used 1DCNN to extract the disturbance features of the phase signal. The phase and intensity vectors within a second are simply normalized by the max-min and tanh functions separately and then fed into the model. The experimental results show that the proposed HDNN model can achieve an average accuracy of 98.8% for four types of events, including human beatings, walking, jumping, and machine excavation, which is better than the 1DCNN model detection result of 96.1% with only a phase signal input. The method using intensity signal-assisted phase signal detection can further improve the accuracy of Φ-OTDR pattern recognition.
