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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.


Training process of the three classifiers.
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sensors-15-15179-f014: Training process of the three classifiers.

Mentions: The event samples acquired using the Φ-OTDR pre-warning system are multiclass and the number of samples is small, so the Relevance Vector Machine (RVM) technique is used in this paper. RVM is a machine learning method based on the Bayesian framework. It is sparser than the Support Vector Machine (SVM) technique; hence it has a shorter recognition time and a higher accuracy [18]. This makes it more suitable for use for recognition for an optical fiber pre-warning system [19,20,21]. The Gauss kernel function is used in this paper because of its widely usability and excellent performance. The kernel parameter is usually set between 0 and 1 [22]. Through experimental analysis, it was found that the highest accuracy was obtained when the parameter of the kernel function was set to 0.6. The RVM technique was designed for two-class classification problems; therefore a one-to-one multi-category method is used for recognition of the three events [23]. Each classifier recognizes two classes, so there are three classifiers for recognition of the three intrusion events. Each classifier is trained with two events and the training process is shown in Figure 14.


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

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

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

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

sensors-15-15179-f014: Training process of the three classifiers.
Mentions: The event samples acquired using the Φ-OTDR pre-warning system are multiclass and the number of samples is small, so the Relevance Vector Machine (RVM) technique is used in this paper. RVM is a machine learning method based on the Bayesian framework. It is sparser than the Support Vector Machine (SVM) technique; hence it has a shorter recognition time and a higher accuracy [18]. This makes it more suitable for use for recognition for an optical fiber pre-warning system [19,20,21]. The Gauss kernel function is used in this paper because of its widely usability and excellent performance. The kernel parameter is usually set between 0 and 1 [22]. Through experimental analysis, it was found that the highest accuracy was obtained when the parameter of the kernel function was set to 0.6. The RVM technique was designed for two-class classification problems; therefore a one-to-one multi-category method is used for recognition of the three events [23]. Each classifier recognizes two classes, so there are three classifiers for recognition of the three intrusion events. Each classifier is trained with two events and the training process is shown in Figure 14.

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.