Recognition of a phase-sensitivity OTDR sensing system based on morphologic feature extraction
Q Sun, H Feng, X Yan, Z Zeng - Sensors, 2015 - mdpi.com
Q Sun, H Feng, X Yan, Z Zeng
Sensors, 2015•mdpi.comThis paper proposes a novel feature extraction method for intrusion event recognition within
a phase-sensitive optical time-domain reflectometer (Φ-OTDR) sensing system. Feature
extraction of time domain signals in these systems is time-consuming and may lead to
inaccuracies due to noise disturbances. The recognition accuracy and speed of current
systems cannot meet the requirements of Φ-OTDR online vibration monitoring systems. In
the method proposed in this paper, the time-space domain signal is used for feature …
a phase-sensitive optical time-domain reflectometer (Φ-OTDR) sensing system. Feature
extraction of time domain signals in these systems is time-consuming and may lead to
inaccuracies due to noise disturbances. The recognition accuracy and speed of current
systems cannot meet the requirements of Φ-OTDR online vibration monitoring systems. In
the method proposed in this paper, the time-space domain signal is used for feature …
This paper proposes a novel feature extraction method for intrusion event recognition within a phase-sensitive optical time-domain reflectometer (Φ-OTDR) sensing system. Feature extraction of time domain signals in these systems is time-consuming and may lead to inaccuracies due to noise disturbances. The recognition accuracy and speed of current systems cannot meet the requirements of Φ-OTDR online vibration monitoring systems. In the method proposed in this paper, the time-space domain signal is used for feature extraction instead of the time domain signal. Feature vectors are obtained from morphologic features of time-space domain signals. A scatter matrix is calculated for the feature selection. Experiments show that the feature extraction method proposed in this paper can greatly improve recognition accuracies, with a lower computation time than traditional methods, i.e., a recognition accuracy of 97.8% can be achieved with a recognition time of below 1 s, making it is very suitable for Φ-OTDR system online vibration monitoring.
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