Simultaneous and signal-to-noise ratio enhancement extraction of vibration location and frequency information in phase-sensitive optical time domain reflectometry distributed sensing system

Yue, Huimin; Zhang, Bo; Wu, Yue; Zhao, Biyu; Li, Jianfeng; Ou, Zhonghua; Liu, Yong

doi:10.1117/1.oe.54.4.047101

Cited by 25 publications

(15 citation statements)

References 10 publications

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“…Afterwards, a Hilbert–Huang transform (HHT) [ 106 ], which has high frequency resolution but is much less time consuming, was introduced to the Φ-OTDR system. In [ 107 ], by applying a 1-D Fourier transform of a Rayleigh backscattering traces matrix in the Φ-OTDR distributed vibration sensing system, the simultaneous extraction of vibration location and frequency information with SNR and SR enhancement was realized. The SR was also improved from 5 to 3.7 m when the pulse duration was 50 ns.…”

Section: Distributed Fiber-optic Vibration Sensing Technologymentioning

confidence: 99%

Distributed Fiber-Optic Sensors for Vibration Detection

Liu

Jin

Bai

et al. 2016

Sensors

186

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Distributed fiber-optic vibration sensors receive extensive investigation and play a significant role in the sensor panorama. Optical parameters such as light intensity, phase, polarization state, or light frequency will change when external vibration is applied on the sensing fiber. In this paper, various technologies of distributed fiber-optic vibration sensing are reviewed, from interferometric sensing technology, such as Sagnac, Mach–Zehnder, and Michelson, to backscattering-based sensing technology, such as phase-sensitive optical time domain reflectometer, polarization-optical time domain reflectometer, optical frequency domain reflectometer, as well as some combinations of interferometric and backscattering-based techniques. Their operation principles are presented and recent research efforts are also included. Finally, the applications of distributed fiber-optic vibration sensors are summarized, which mainly include structural health monitoring and perimeter security, etc. Overall, distributed fiber-optic vibration sensors possess the advantages of large-scale monitoring, good concealment, excellent flexibility, and immunity to electromagnetic interference, and thus show considerable potential for a variety of practical applications.

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Section: Distributed Fiber-optic Vibration Sensing Technologymentioning

confidence: 99%

Distributed Fiber-Optic Sensors for Vibration Detection

Liu

Jin

Bai

et al. 2016

Sensors

186

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“…Hence, it provides an efficient way for the monitoring of multi-point acoustic vibrations along the cable. Its effectiveness has been demonstrated in various applications such as crack detection of structures [4,5], railroad monitoring [6,7], traffic flow detection [8], vehicle detection [9], and intrusion detection [10][11][12][13][14][15][16][17][18].…”

Section: Introductionmentioning

confidence: 99%

Variational Mode Decomposition-Based Threat Classification for Fiber Optic Distributed Acoustic Sensing

Abufana

Dalveren

Aghnaiya³

et al. 2020

IEEE Access

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In this study, a novel method is proposed to detect and classify the threats for fiber optic distributed acoustic sensing (DAS) systems. In the study, phase-sensitive optical time-domain reflectometry (phase-OTDR) is realized for the sensing system. The proposed method is consisted of three main stages. In the first stage, Wavelet denoising method is applied for noise reduction in the measured signal, and difference in time domain approach is used to perform high-pass filtering. Autocorrelation is then used for comparing the signal with itself over time in each bin to remove uncorrelated signals. Next, the power of the correlated signals at each bin is calculated and sorted where maximum valued bins are considered as the event signal. In the second stage, Variational Mode Decomposition (VMD) technique is used to decompose the detected event signals into a series of band-limited modes from which the event signals are reconstructed. From the reconstructed event signals, higher order statistical (HOS) features including variance, skewness, and kurtosis are extracted. In the last stage, the threats are discriminated by implementing Linear Support Vector Machine (LSVM)-based classification approach to the extracted features. In order to evaluate the effects of proposed method on the classification performance, different types of activities such as digging with hammer, pickaxe, and shovel collected from various points of a buried fiber optic cable have been used under different Signalto-Noise Ratio (SNR) levels (-4 to-18 dB). It has observed that the classification accuracy at high/moderate (-4 to-8 dB) and low (-8 to-18 dB) SNR levels are 79.5% and 75.2%, respectively. To the best of authors' knowledge, this research study is the first report to use VMD technique for threat classification in phase-OTDR-based DAS systems.

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“…On the other hand, the noise elimination is mainly dependent on a signal processing algorithm in time domain and frequency domain [16][17][18][19]. A fast Fourier transform is performed on the original signal matrix to separate the noise and vibration signals in the frequency domain to obtain a high SNR for the vibration location [16]. An active frequency scanning method is combined with a cross-correlation algorithm to dynamically reduce the noise caused by frequency drift [17].…”

Section: Introductionmentioning

confidence: 99%

Optical Fiber Vibration Sensor Using Least Mean Square Error Algorithm

Wang

Zou

et al. 2020

Sensors

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In order to enhance the signal-to-noise ratio (SNR) of a distributed optical fiber vibration sensor based on coherent optical time domain reflectometry (COTDR), a high extinction ratio cascade structure of an acousto-optic modulator and semiconductor optical amplifier is applied. The prior time-frequency analysis and least mean square error algorithm are adopted in the COTDR system for amplitude demodulation and phase demodulation, in order to improve the SNR by noise elimination. The experimental results show that the adaptive filter based on the least mean square error algorithm could realize the extraction of a three-order sinusoidal harmonic signal from strong background noise along the optical fiber and the SNR improvement from 10.4 dB to 42.2 dB. The proposed demodulation algorithm is suitable for the detection of vibration signals with characteristic frequencies in the application of acoustic fault diagnosis for electromechanical devices.

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Simultaneous and signal-to-noise ratio enhancement extraction of vibration location and frequency information in phase-sensitive optical time domain reflectometry distributed sensing system

Cited by 25 publications

References 10 publications

Distributed Fiber-Optic Sensors for Vibration Detection

Distributed Fiber-Optic Sensors for Vibration Detection

Variational Mode Decomposition-Based Threat Classification for Fiber Optic Distributed Acoustic Sensing

Optical Fiber Vibration Sensor Using Least Mean Square Error Algorithm

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