An oil and gas pipeline pre-warning system based on Φ-OTDR

Tan, Dongjie; Tian, Xiaozhong; Sun, Wei; Zhou, Yan; Liu, Lu; Ma, Yunbing; Jia, Meng; Zhang, Hong

doi:10.1117/12.2054698

Cited by 29 publications

(10 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Since the signals and the noises are uncorrelated, the eigenvectors corresponding to the noises are orthogonal to the eigenvectors of the signals. Then the spatial spectral function can be constructed with and k as, In (6), when a proper ( ) is found orthogonal to the noise vector, the spatial spectrum mn2 reaches its maximum value. We change by a small increment of 0.1 degree each time in a range of zero to 90 degrees, and the proper DOA can be determined by the peak searching of the spectrum.…”

Section: Challenges Of the Feature Subspace Related Methodsmentioning

confidence: 99%

“…As a typical distributed optical fiber sensor (DOFS), distributed acoustic sensor (DAS) based on the phasesensitive optical time-domain reflectometry (Φ-OTDR) technology [1,2], provides a long distance, highly sensitive, and cost-effective continuous measurement way for our environment, including seismic monitoring [3,4], intrusion detection [5], oil or gas pipe security [6], and fiber optic communication cable network security [7] and so on. With the existing communication optical cables, the vibration generated by various destructive sources in the surrounding environment can be sensed and located by DAS with high precision.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Vertical Offset-Distance Estimation and Threat Level Prediction of Vibrations With DAS

Yang

et al. 2020

IEEE Access

View full text Add to dashboard Cite

In applications of the typical distributed optical fiber sensor, DAS, it is increasingly appealing to mine deeper information to aid in accurate decision making besides basic functions of detection, positioning and identification. Thus a collaborative energy-based method of source localization is proposed to estimate the vertical offset-distance of a specific vibration source and its threat level to the buried fiber. The contributions of this paper include: 1) It is for the first time to provide a feasible method to make threat level prediction and offset-distance estimation in the underground buried environments with DAS; 2) The spatial energy distribution characteristics of the vibration source is first discovered to have relationship with its attenuation law at different vertical offset-distances, which can be used to predict the threat level; 3) A two-stacking machine learning method is constructed to automatically discriminate the differences in the extracted collaborative energy distribution features of different distant vibrations. The effectiveness of this method is proved in the field test for the vibration source localization in the underground buried environments and useful discussions are included.

show abstract

Section: Challenges Of the Feature Subspace Related Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Vertical Offset-Distance Estimation and Threat Level Prediction of Vibrations With DAS

Yang

et al. 2020

IEEE Access

View full text Add to dashboard Cite

show abstract

“…Distributed Acoustic Sensor (DAS) based on the phasesensitive optical time-domain reflectometry (Φ-OTDR) technology [1]- [4], provides a highly sensitive and costeffective vibration or sound sensing way for our environment in long distance or wide range. It has been extensively applied in many safety monitoring fields, such as natural disaster prediction including seismic waves [5], the third party damage warning for oil/gas/water/heating pipelines [6], [7], and perimeter security [8], [9] and large structure health monitoring, etc. Especially, it is a promising technology in maintenance of urban communication cables [10].…”

Section: Introductionmentioning

confidence: 99%

“…However, intelligent recognition and deep understanding of real threats such as mechanical operations, manual diggings and other vibration sources as the traffic interferences along the fiber is still a challenging problem, which results in frequent nuisance alarms in practical applications. In fact, to improve its smart sensing ability, a lot of useful signal processing work has been involved, which includes conventional machine learning methods with fixed hand-crated feature extraction [6], [7], [11], [12], and deep learning methods with one dimensional(1-D) [13] or two dimensional (2-D) Convolution Neural Networks (CNNs) [14]- [16] to extract the hidden distinguishable feature automatically.…”

Section: Introductionmentioning

confidence: 99%

A Novel DAS Signal Recognition Method Based on Spatiotemporal Information Extraction With 1DCNNs-BiLSTM Network

Yang

et al. 2020

IEEE Access

View full text Add to dashboard Cite

Extracting more and more accurate information to understand the detected vibration or acoustic targets better, has always been an important goal in signal recognition for Distributed Acoustic Sensor(DAS) with optical fiber. In this paper, we use one-dimensional Convolution Neural Networks(1D-CNNs) to extract the detailed temporal structure information at each signal node and utilize a bidirectional Long Short Term Memory(BiLSTM) network to dig out the spatial relationship among the different signal nodes, and then propose a novel identification method by treating the spatial-and temporalinformation in a different way, which is denoted as the 1DCNNs-BiLSTM model. The experimental results on the field data show better recognition performance can be achieved in the safety monitoring of the buried optical communication cable in urban with DAS. It helps to improve the recognition rate further compared with the other deep-learning methods frequently or possibly used for DAS signal recognition, such as the 1D-CNNs with a single temporal feature extraction, and 1DCNN-CNN and 2D-CNN models with simultaneous spatiotemporal feature learning. To the best of our knowledge, it is the first time to simultaneously extract and utilize the detailed temporal structure feature and the overall spatial connection through a customized deep learning network.

show abstract

“…The Φ-OTDR is one kind of DOFS's, which has been widely used for heath monitoring [7][8][9] due to the advantages of high sensitivity and fast response. In the Φ-OTDR, the backward Rayleigh scattering curve exhibits a jagged appearance [10] due to the coherent superposition of the signal generated from massive scattering centers within the optical pulse duration, because of the long coherent length of the laser source.…”

Section: Introductionmentioning

confidence: 99%

Distributed Strain and Vibration Sensing System Based on Phase-Sensitive OTDR

Zhou

Wang

et al. 2015

IEEE Photon. Technol. Lett.

View full text Add to dashboard Cite

A system based on phase-sensitive optical time domain reflectometry (Φ-OTDR) is proposed for simultaneously strain and vibration sensing. The strain of fiber is detected by comparing the patterns of signal for different laser frequencies, and the vibration of fiber is detected simultaneously from the signals for any certain laser frequency. During the measurement, frequencies of the probe optical pulses are modulated sequentially in ascending or descending order. Using the signals generated by optical pulses with the same frequency, the vibration of fiber is detected with fast response speed; using that with different frequencies, the strain of fiber is detected with high resolution. In our experiment, a sensing system with 2 m spatial resolution, up to 1 kHz frequency measurement range and 10 n strain resolution is realized for a 9 km sensing fiber length. Index Terms-Distributed optical fiber sensor, phase-sensitive optical time domain reflectometry, strain measurement, vibration measurement

show abstract

An oil and gas pipeline pre-warning system based on Φ-OTDR

Cited by 29 publications

References 2 publications

Vertical Offset-Distance Estimation and Threat Level Prediction of Vibrations With DAS

Vertical Offset-Distance Estimation and Threat Level Prediction of Vibrations With DAS

A Novel DAS Signal Recognition Method Based on Spatiotemporal Information Extraction With 1DCNNs-BiLSTM Network

Distributed Strain and Vibration Sensing System Based on Phase-Sensitive OTDR

Contact Info

Product

Resources

About