A Fiber-sensor-based Long-distance Safety Monitoring System for buried Oil Pipeline

Wang, Li; Wang, Yaohui; Xiao, Xin-Lu; Hu, Yan; Shi, Guangshun; Wang, Qingren

doi:10.1109/icnsc.2008.4525259

Cited by 8 publications

(6 citation statements)

References 3 publications

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“…As the extension of our previous work [7,8], this paper focus on making the perimeter security system more intelligent, namely, it can identify more damaging activities. Using vibration signals collected by fiber sensor as data source, we apply both statistical analysis by Gram-Charlier series and time-frequency analysis by wavelet packet decomposition to get a feature vector of each vibration signal fragment.…”

Section: Introductionmentioning

confidence: 99%

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Identification of Damaging Activities for Perimeter Security

Shi

Wang

et al. 2009

2009 International Conference on Signal Processing Systems

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The advent of fiber sensor has opened up a plenty of opportunities to perimeter security. Damaging activities can cause fiber vibrate whose signals will be collected as data source for detection and identification. In this paper we formulate identifying damaging activities by vibration signals as a classification problem. We design features that characterize vibration signals by combining both the statistic and time-frequency information. In addition, a novel multiclass classification tree of Support Vector Machine (SVM) is introduced to recognize vibration signals. Experimental results show that the proposed feature extraction scheme and classification method yields a high recognition rate of 94.6% for nine different kinds of damaging activities, much better than other results reported ever since.

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Section: Introductionmentioning

confidence: 99%

“…Unfortunately, the system, with low intelligence, can not tell which kind of damaging activity has happened. Wang [7] presents a fibersensor-based long-distance safety monitoring system for buried oil pipeline. More over, he analyzes vibration signals.…”

Section: Introductionmentioning

confidence: 99%

Identification of Damaging Activities for Perimeter Security

Shi

Wang

et al. 2009

2009 International Conference on Signal Processing Systems

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“…To develop a non-slippage FO, the bonding and point fixation methods were investigated experimentally and the critical effective sensing length for long-gauge fiber was studied [23]. A combination between an artificial neural network and a distributed FO vibration sensing (DOFVS) system based on long-distance fiber has been used to collect the responses in the vibration signal of soil around a pipeline [24][25][26][27]. In addition, the technology of the DOFVS system based on a phase-sensitive optical time-domain reflectometer (OTDR) was used to develop a water pipeline hydrostatic leak test [28] and fatigue damage identification for composite pipelines systems using electrical capacitance sensors [29].…”

Section: Introductionmentioning

confidence: 99%

Structural Health Monitoring of Composite Pipelines Utilizing Fiber Optic Sensors and an AI-Based Algorithm—A Comprehensive Numerical Study

Altabey

Noori

et al. 2023

Sensors

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In this paper, a structural health monitoring (SHM) system is proposed to provide automatic early warning for detecting damage and its location in composite pipelines at an early stage. The study considers a basalt fiber reinforced polymer (BFRP) pipeline with an embedded Fiber Bragg grating (FBG) sensory system and first discusses the shortcomings and challenges with incorporating FBG sensors for accurate detection of damage information in pipelines. The novelty and the main focus of this study is, however, a proposed approach that relies on designing an integrated sensing-diagnostic SHM system that has the capability to detect damage in composite pipelines at an early stage via implementation of an artificial intelligence (AI)-based algorithm combining deep learning and other efficient machine learning methods using an Enhanced Convolutional Neural Network (ECNN) without retraining the model. The proposed architecture replaces the softmax layer by a k-Nearest Neighbor (k-NN) algorithm for inference. Finite element models are developed and calibrated by the results of pipe measurements under damage tests. The models are then used to assess the patterns of the strain distributions of the pipeline under internal pressure loading and under pressure changes due to bursts, and to find the relationship of strains at different locations axially and circumferentially. A prediction algorithm for pipe damage mechanisms using distributed strain patterns is also developed. The ECNN is designed and trained to identify the condition of pipe deterioration so the initiation of damage can be detected. The strain results from the current method and the available experimental results in the literature show excellent agreement. The average error between the ECNN data and FBG sensor data is 0.093%, thus confirming the reliability and accuracy of the proposed method. The proposed ECNN achieves high performance with 93.33% accuracy (P%), 91.18% regression rate (R%) and a 90.54% F1-score (F%).

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“…The leakage of the oil pipeline endangers the safety of the transportation system and even human life. Therefore, it is of great necessity to develop pipeline leakage monitoring methods in order to detect the leakage in real time (Wang et al, 2008).…”

Section: Introductionmentioning

confidence: 99%

An improved two-dimensional variational mode decomposition algorithm and its application in oil pipeline image

Gao

Dong

et al. 2020

Systems Science & Control Engineering

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In this paper, an image denoising algorithm is presented based on the two-dimensional variational mode decomposition (2D-VMD) and the Hausdorff distance (HD). The procedure of the developed algorithm is that: (1) use the 2D-VMD to decompose the image into a number of intrinsic mode functions (IMFs); (2) use the HD and the probability density function (PDF) to distinguish the signaldominant IMFs (S-D IMFs) and the noise-dominant IMFs (N-D IMFs), and then use the wavelet threshold denoising method to eliminate the noise in the N-D IMFs; and (3) use the denoised IMFs and the S-D IMFs to reconstruct the image and obtain the denoised image. The effectiveness of the proposed algorithm is verified on the oil pipeline images. Simulation results show that, compared with the median filtering, the wavelet threshold filtering, and the Rudin Osher Fatemi, the proposed algorithm has a better denoising effect in both subjective and objective evaluation.

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A Fiber-sensor-based Long-distance Safety Monitoring System for buried Oil Pipeline

Cited by 8 publications

References 3 publications

Identification of Damaging Activities for Perimeter Security

Identification of Damaging Activities for Perimeter Security

Structural Health Monitoring of Composite Pipelines Utilizing Fiber Optic Sensors and an AI-Based Algorithm—A Comprehensive Numerical Study

An improved two-dimensional variational mode decomposition algorithm and its application in oil pipeline image

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