An Event Recognition Method for Φ-OTDR Sensing System Based on Deep Learning

Shi, Yi; Wang, Yuanye; Zhao, Lei; Fan, Zhun

doi:10.3390/s19153421

Cited by 115 publications

(50 citation statements)

References 26 publications

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“…But the accuracy of walking through the fiber and jogging along the fiber were both under 85%. In the same year, Shi et al [17] used the data matrix obtained by bandpass filtering and grayscale conversion preprocessing of the original spatiotemporal signal as the input of CNN. By simplifying the structure of GoogLeNet, the running memory was reduced and running speed was improved.…”

Section: Introductionmentioning

confidence: 99%

Multi-Class Disturbance Events Recognition Based on EMD and XGBoost in φ-OTDR

et al. 2020

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A novel pattern recognition method based on Empirical Mode Decomposition (EMD) and extreme gradient boosting (XGBoost) is proposed to recognize the disturbance events in phase sensitive optical time-domain reflectometer (ϕ-OTDR) to reduce nuisance alarm rate (NAR) and improve real-time performance in this paper. Eleven typical eigenvectors are extracted from components obtained by EMD of the disturbance signals and XGBoost is selected as a classifier to identify different type of disturbance signals. Five kinds of disturbance events, including watering, knocking, climbing, pressing and false disturbance event, can be identified, effectively. Experimental results show that NAR is 4.10% and identification time is 0.093 s. The recognition accuracy for the five patterns is 97.96%, 95.90%, 91.10%, 94.84% and 99.69%, respectively. The effectiveness of the proposed method is evaluated by using confusion matrix and decision boundary visualization. Experimental results demonstrate that our proposed pattern recognition method based on XGBoost has better performance in recognition rate and recognition time than other commonly used methods, such as support vector machine (SVM), Gradient Boosting Decision Tree (GBDT), Random Forest (RF) and Adaptive Boosting (Adaboost). INDEX TERMS Phase-sensitive optical time-domain reflectometer (ϕ-OTDR), extreme gradient boosting (XGBoost), nuisance alarm rate (NAR), empirical mode decomposition (EMD), pattern recognition, decision boundary visualization.

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Section: Introductionmentioning

confidence: 99%

Multi-Class Disturbance Events Recognition Based on EMD and XGBoost in φ-OTDR

et al. 2020

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“…For the filtered signals, the signal-to-noise ratio was determined as the ratio of the root–mean–square deviation (RMS) of the area’s signal to the impact of the RMSD for the signal in the unimpacted area. A comparison of RMSD values is more useful for detection algorithms applications [ 57 , 58 , 59 ] than a comparison of spectral peak’s level [ 46 ]. The data were filtered with a second-order Butterworth bandpass filter in the range from 18 to 22 Hz.…”

Section: Resultsmentioning

confidence: 99%

The Sensitivity Improvement Characterization of Distributed Strain Sensors Due to Weak Fiber Bragg Gratings

Stepanov

Zhirnov

Chernutsky

et al. 2020

Sensors

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Weak fiber Bragg gratings (WFBGs) in a phase-sensitive optical time-domain reflectometer (phi-OTDR) sensor offer opportunities to significantly improve the signal-to-noise ratio (SNR) and sensitivity of the device. Here, we demonstrate the process of the signal and noise components’ formation in the device reflectograms for a Rayleigh scattering phi-OTDR and a WFBG-based OTDR. We theoretically calculated the increase in SNR when using the same optical and electrical components under the same external impacts for both setups. The obtained values are confirmed on experimental installations, demonstrating an improvement in the SNR by about 19 dB at frequencies of 20, 100, and 400 Hz. In this way, the minimum recorded impact (at the threshold SNR = 10) can be reduced from 100 nm per 20 m of fiber to less than 5 nm per 20 m of fiber sensor.

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“…In addition, some pattern recognition methods are implemented with different classifiers and feature vectors [ 85 ], including, Gaussian mixture model [ 7 , 86 ], support vector machine [ 87 ]. With the development, some deep learning methods are also introduced [ 3 , 88 ].…”

Section: Applicationsmentioning

confidence: 99%

Recent Progress in Distributed Fiber Acoustic Sensing with Φ-OTDR

Wang

Lü

et al. 2020

Sensors

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Distributed fiber acoustic sensing (DAS) technology can continuously spatially detect disturbances along the sensing fiber over long distance in real time. It has many unique advantages, including, large coverage, high time-and-space resolution, convenient implementation, strong environment adaptability, etc. Nowadays, DAS becomes a versatile technology in many fields, such as, intrusion detection, railway transportation, seismology, structure health monitoring, etc. In this paper, the sensing principle and some common performance indexes are introduced, and a brief overview of recent DAS researches in Shanghai Institute of Optics and Fine Mechanics (SIOM) is presented. Some representative research advances are explained, including, quantitative demodulation, interference fading suppression, frequency response boost, high spatial resolution, and distributed multi-dimension localization. The engineering applications of DAS, carried out by SIOM and other groups, are summarized and reviewed. Finally, possible future directions are discussed and concluded. It is believed that, DAS has great development potential and application prospect.

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An Event Recognition Method for Φ-OTDR Sensing System Based on Deep Learning

Cited by 115 publications

References 26 publications

Multi-Class Disturbance Events Recognition Based on EMD and XGBoost in φ-OTDR

Multi-Class Disturbance Events Recognition Based on EMD and XGBoost in φ-OTDR

The Sensitivity Improvement Characterization of Distributed Strain Sensors Due to Weak Fiber Bragg Gratings

Recent Progress in Distributed Fiber Acoustic Sensing with Φ-OTDR

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