Mixed Intrusion Events Recognition Based on Group Convolutional Neural Networks in DAS System

Yan, Sen; Shang, Ying; Wang, Chen; Zhao, Wei; Ni, Jiasheng

doi:10.1109/jsen.2021.3129473

Cited by 16 publications

(3 citation statements)

References 14 publications

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“…The results obtained in this work can be applied to a wide range of problems. First, the presence of an inexpensive laser in the monitoring system will make it available not only for geological exploration, monitoring of pipelines, and mines, but also for new applications such as urban traffic monitoring and security systems [70][71][72][73]. New areas will open up where DAS technology can be applied.…”

Section: Discussionmentioning

confidence: 99%

Enhancing the Distributed Acoustic Sensors’ (DAS) Performance by the Simple Noise Reduction Algorithms Sequential Application

et al. 2023

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Moving differential and dynamic window moving averaging are simple and well-known signal processing algorithms. However, the most common methods of obtaining sufficient signal-to-noise ratios in distributed acoustic sensing use expensive and precise equipment such as laser sources, photoreceivers, etc., and neural network postprocessing, which results in an unacceptable price of an acoustic monitoring system for potential customers. This paper presents the distributed fiber-optic acoustic sensors data processing and noise suppression techniques applied both to raw data (spatial and temporal amplitude distributions) and to spectra obtained after the Fourier transform. The performance of algorithms’ individual parts in processing distributed acoustic sensor’s data obtained in laboratory conditions for an optical fiber subjected to various dynamic impact events is studied. A comparative analysis of these parts’ efficiency was carried out, and for each type of impact event, the most beneficial combinations were identified. The feasibility of existing noise reduction techniques performance improvement is proposed and tested. Presented algorithms are undemanding for computation resources and provide the signal-to-noise ratio enhancement of up to 13.1 dB. Thus, they can be useful in areas requiring the distributed acoustic monitoring systems’ cost reduction as maintaining acceptable performance while allowing the use of cheaper hardware.

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Section: Discussionmentioning

confidence: 99%

Enhancing the Distributed Acoustic Sensors’ (DAS) Performance by the Simple Noise Reduction Algorithms Sequential Application

et al. 2023

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“…Later developments showed that the demodulation of the phase will help us to obtain the waveform of the vibration with high definition, which is the basis of distributed acoustic sensing (DAS) [ 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 ]. DAS has been rapidly commercialized and widely considered in many industrial applications, including pipeline monitoring [ 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 ], railway and highway transportation [ 23 , 40 , 41 , 42 , 43 , 44 , 45 , 46 ], structural inspection [ 47 , 48 , 49 , 50 ], perimeter security [ 51 , 52 , 53 , 54 , 55 ], geophysics [ 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 , 65 , 66 , 67 , 68 ], and biology and natural sciences [ 69 , 70 , 71 ]. Brillouin scattering light is characterized by the linear relationship with strain and temperature in the frequency domain [ 72 ,…”

Section: Introductionmentioning

confidence: 99%

“…Later developments showed that the demodulation of the phase will help us to obtain the waveform of the vibration with high definition, which is the basis of distributed acoustic sensing (DAS) [24][25][26][27][28][29][30][31]. DAS has been rapidly commercialized and widely considered in many industrial applications, including pipeline monitoring [32][33][34][35][36][37][38][39], railway and highway transportation [23,[40][41][42][43][44][45][46], structural inspection [47][48][49][50], perimeter security [51][52][53][54][55], geophysics [56][57][58][59][60][61][62][63][64][65][66][67][68], and biology and natural sciences [69][70...…”

Section: Introductionmentioning

confidence: 99%

Hybrid Distributed Optical Fiber Sensor for the Multi-Parameter Measurements

Zhou

Wang

Yang

et al. 2023

Sensors

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Distributed optical fiber sensors (DOFSs) are a promising technology for their unique advantage of long-distance distributed measurements in industrial applications. In recent years, modern industrial monitoring has called for comprehensive multi-parameter measurements to accurately identify fault events. The hybrid DOFS technology, which combines the Rayleigh, Brillouin, and Raman scattering mechanisms and integrates multiple DOFS systems in a single configuration, has attracted growing attention and has been developed rapidly. Compared to a single DOFS system, the multi-parameter measurements based on hybrid DOFS offer multidimensional valuable information to prevent misjudgments and false alarms. The highly integrated sensing structure enables more efficient and cost-effective monitoring in engineering. This review highlights the latest progress of the hybrid DOFS technology for multi-parameter measurements. The basic principles of the light-scattering-based DOFSs are initially introduced, and then the methods and sensing performances of various techniques are successively described. The challenges and prospects of the hybrid DOFS technology are discussed in the end, aiming to pave the way for a vaster range of applications.

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Method of extracting disturbed position in φ-OTDR based on signal relevance evaluation

Li,

Yang,

Liu

et al. 2024

Optoelectron. Lett.

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Mixed Intrusion Events Recognition Based on Group Convolutional Neural Networks in DAS System

Cited by 16 publications

References 14 publications

Enhancing the Distributed Acoustic Sensors’ (DAS) Performance by the Simple Noise Reduction Algorithms Sequential Application

Enhancing the Distributed Acoustic Sensors’ (DAS) Performance by the Simple Noise Reduction Algorithms Sequential Application

Hybrid Distributed Optical Fiber Sensor for the Multi-Parameter Measurements

Method of extracting disturbed position in φ-OTDR based on signal relevance evaluation

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