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Recognition of a Phase-Sensitivity OTDR Sensing System Based on Morphologic Feature Extraction.

Sun Q, Feng H, Yan X, Zeng Z - Sensors (Basel) (2015)

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: Tianjin University, State Key Laboratory of Precision Measurement Technology & Instruments, 92 Weijin Road, Nankai District, Tianjin 300072, China. sunqiansohu@126.com.

ABSTRACT
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.

No MeSH data available.


Parameters of Equation (6).
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sensors-15-15179-f003: Parameters of Equation (6).

Mentions: In Equation (6), α is the phase attenuation coefficient, s0 is the position of the intrusion event and d is the range of the intrusion force. is the phase variation in the time domain. The schematic diagram is shown in Figure 3.


Recognition of a Phase-Sensitivity OTDR Sensing System Based on Morphologic Feature Extraction.

Sun Q, Feng H, Yan X, Zeng Z - Sensors (Basel) (2015)

Parameters of Equation (6).
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4541826&req=5

sensors-15-15179-f003: Parameters of Equation (6).
Mentions: In Equation (6), α is the phase attenuation coefficient, s0 is the position of the intrusion event and d is the range of the intrusion force. is the phase variation in the time domain. The schematic diagram is shown in Figure 3.

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: Tianjin University, State Key Laboratory of Precision Measurement Technology & Instruments, 92 Weijin Road, Nankai District, Tianjin 300072, China. sunqiansohu@126.com.

ABSTRACT
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.

No MeSH data available.