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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.


Recognition process of three classifiers.
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sensors-15-15179-f015: Recognition process of three classifiers.

Mentions: During the recognition process, an unknown event is recognized by the three classifiers. If two classifiers have the same results, the event will belong to that class. The recognition process is shown in Figure 15.


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

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

Recognition process of three classifiers.
© Copyright Policy
Related In: Results  -  Collection

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

sensors-15-15179-f015: Recognition process of three classifiers.
Mentions: During the recognition process, an unknown event is recognized by the three classifiers. If two classifiers have the same results, the event will belong to that class. The recognition process is shown in Figure 15.

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.