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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.


Figure1. Scattering curve of intrusion signal.
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sensors-15-15179-f001: Figure1. Scattering curve of intrusion signal.

Mentions: Due to the nonlinear, dynamic nature of the signal acquired by a Φ-OTDR vibration sensing system, a location scheme based on the wavelet packet transform (WPT) is proposed to reduce the number of false alarms [7]. Previous studies have used the short-time Fourier transform (STFT) and the continuous wavelet transform (CWT) for recognition of Φ-OTDR systems [8]. These traditional methods focus on finding and locating the intrusion events. If the features of the time domain signal can be extracted after the event has been located, different event types can be classified based on these signal features. However, this method is time consuming, due to the requirement to firstly pinpoint the location in the recognition process. If multiple events occur simultaneously, the recognition time will increase significantly. The intrusion signal of an Φ-OTDR system is not a single point in the space domain; it occurs across a range, since the attenuation will continue for a period along the optic fiber. Within the attenuation range, each scattered light signal contains the vibration response, but since the initial phase of each interference signal is different, the amplitudes of the vibration responses are different within the attenuation range, as shown in Figure 1. The peaks are due to backscattering when an intrusion event occurs. However, the maximum point may not be the location of the intrusion. If the event location is identified as the point with the maximum light intensity, then an error in location will occur.


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

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

Figure1. Scattering curve of intrusion signal.
© Copyright Policy
Related In: Results  -  Collection

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

sensors-15-15179-f001: Figure1. Scattering curve of intrusion signal.
Mentions: Due to the nonlinear, dynamic nature of the signal acquired by a Φ-OTDR vibration sensing system, a location scheme based on the wavelet packet transform (WPT) is proposed to reduce the number of false alarms [7]. Previous studies have used the short-time Fourier transform (STFT) and the continuous wavelet transform (CWT) for recognition of Φ-OTDR systems [8]. These traditional methods focus on finding and locating the intrusion events. If the features of the time domain signal can be extracted after the event has been located, different event types can be classified based on these signal features. However, this method is time consuming, due to the requirement to firstly pinpoint the location in the recognition process. If multiple events occur simultaneously, the recognition time will increase significantly. The intrusion signal of an Φ-OTDR system is not a single point in the space domain; it occurs across a range, since the attenuation will continue for a period along the optic fiber. Within the attenuation range, each scattered light signal contains the vibration response, but since the initial phase of each interference signal is different, the amplitudes of the vibration responses are different within the attenuation range, as shown in Figure 1. The peaks are due to backscattering when an intrusion event occurs. However, the maximum point may not be the location of the intrusion. If the event location is identified as the point with the maximum light intensity, then an error in location will occur.

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.