Sensor Motes for the Exploration and Monitoring of Operational Pipelines

Duisterwinkel, Erik H. A.; Talnishnikh, Elena; Krijnders, Dirkjan; Wörtche, H. J.

doi:10.1109/tim.2017.2775404

Cited by 30 publications

(17 citation statements)

References 5 publications

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“…where the variance σ(t) varies with the third-derivative of the phase function ϕ(t) and the second-derivative of estimated IF r (m) (t). Since the difference ϕ (3)…”

Section: Performance Analysismentioning

confidence: 99%

“…To prevent the danger of oil and gas leakage caused by pipeline damage, we need to check the pipeline regularly to ensure the safety and health of the pipeline. The routine method of long-distance pipelines inspection is the In-Line Inspection (ILI) [2], such as Ultrasonic Testing [3] and Magnetic Flux Leakage testing [4,5]. However, most of the small diameter pipes and urban underground pipelines have characteristics that restrict the piping, therefore non-contact detection of pipeline defects is an urgent problem to be solved.…”

Section: Introductionmentioning

confidence: 99%

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Variational Specific Mode Extraction: A Novel Method for Defect Signal Detection of Ferromagnetic Pipeline

Wang

Zhao

2020

Algorithms

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The non-contact detection of buried ferromagnetic pipeline is a long-standing problem in the field of inspection of outside pipelines, and the extraction of magnetic anomaly signal is a prerequisite for accurate detection. Pipeline defects can cause the fluctuation of magnetic signals, which are easily submerged in wide-band background noise without external excitation sources. Previously, Variational Mode Decomposition (VMD) was used to separate modal components; however, VMD is based on narrow-band signal processing algorithm and the calculation is complex. In this article, a method of pipeline defect signal based on Variational Specific Mode Extraction (VSME) is employed to extract the signal of a specific central frequency by signal modal decomposition, i.e., the specific mode is weak magnetic anomaly signal of pipeline defects. VSME is based on the fact that a wide-band signal can be converted into a narrow-band signal by demodulation method. Furthermore, the problem of wide-band signal decomposition is expressed as an optimal demodulation problem, which can be solved by alternating direction method of multipliers. The proposed algorithm is verified by artificially synthesized signals, and its performance is better than that of VMD. The results showed that the VSME method can extract the magnetic anomaly signal of pipeline damage using experimental data, while obtaining a better accuracy.

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“…where the variance σ(t) varies with the third-derivative of the phase function ϕ(t) and the second-derivative of estimated IF r (m) (t). Since the difference ϕ (3)…”

Section: Performance Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Variational Specific Mode Extraction: A Novel Method for Defect Signal Detection of Ferromagnetic Pipeline

Wang

Zhao

2020

Algorithms

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“…Technological advances played a pivotal role in leveraging the use of miniature wireless agents for novel application cases. Among these are for example scenarios where millimeter-sized agents are employed for pipeline inspection [1][2][3] or the exploration of difficult-to-access environments [4]. In the former case, agents are deployed to monitor the physical state of the piping system, i.e., to analyze the pipes for physical damages.…”

Section: Introductionmentioning

confidence: 99%

Random field-aided tracking of autonomous kinetically passive wireless agents

Schlupkothen

Heidenblut

Ascheid

2020

EURASIP J. Adv. Signal Process.

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Continuous miniaturization of circuitry has open the door for various novel application scenarios of millimeter-sized wireless agents such as for the exploration of difficult-to-access fluid environments. In this context, agents are envisioned to be employed, e.g., for pipeline inspection or groundwater analysis. In either case, the demand for miniature sensors is incompatible with propulsion capabilities. Consequently, the agents are condemned to be kinetically passive and are, thus, subject to the fluid dynamics present in the environment. In these situations, the localization is complicated by the fact that unknown external forces (e.g., from the fluid) govern the motion of the agents. In this work, a comprehensive framework is presented that targets the simultaneous estimation of the external forces stemming from the fluid and the agents' positions which are traversing the environment. More precisely, a Bayesian hierarchical model is proposed that models' relevant characteristics of the fluid via a spatial random field and incorporates this as control input into the motion model. The random field model facilitates the consideration of spatial correlation among the agents' trajectories and, thereby, improves the localization significantly. Additionally, this is combined with multiple particle filtering to account for the fact that within such underground fluid environments, only a localization based on distance and/or bearing measurements is feasible. In the results provided in this work, which are based on realistic computational fluid dynamics simulations, it is shown that-via the proposed spatial model-significant improvements in terms of localization accuracy can be achieved.

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“…These inspections are normally carried out using non-destructive testing and evaluation (NDT&E) [3]. In the area of NDT&E, a range of inspection methods are available for pipeline integrity that include ultrasonic technique [4] [5], pulsed eddy current [6] [7], magnetic flux leakage M. D. Buhari and G. Y. Tian are with the Department of Electrical and Electronic Engineering, Newcastle University, Newcastle Upon Tyne, NE1 7RU, and R. Tiwari is with NSL Nottingham.…”

Section: Introductionmentioning

confidence: 99%

Microwave-Based SAR Technique for Pipeline Inspection Using Autofocus Range-Doppler Algorithm

2019

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In non-destructive testing and evaluation, microwave-based synthetic aperture radar (SAR) imaging have shown great potential in the detection of defects on buried objects such as pipes. However, due to pipe curvature and high standoff distance when inspecting an insulated pipe, the useful defect information used to characterise the pipe image is lost as a result of low signal-to-noise ratio (SNR) resulting in a blurred and unfocused image. In this paper, we proposed a robust microwave-based SAR imaging using autofocus range-Doppler algorithm (RDA) for the inspection of an insulated pipe. Singular value decomposition (SVD) is used to mitigate the effect of the insulation layer by removing dominant singular values that characterise the insulation layer, and the autofocus RDA is designed to refocus the SAR image using RDA residual refocusing. SNR, improvement factor (IF) and squared error (SE) are used to evaluate the qualitative image information of the defect on the pipe. Experimental results showed the efficacy of the method in detecting defects on an insulated pipe, in particular, a significant reduction in the noise content of the image compared to the known SAR Omega-k algorithm. It was found that the autofocus RDA gave higher values of SNR and IF (3 dB and 6 dB) compared to the Omega-k algorithm (-1 dB and 2 dB) respectively.

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Sensor Motes for the Exploration and Monitoring of Operational Pipelines

Cited by 30 publications

References 5 publications

Variational Specific Mode Extraction: A Novel Method for Defect Signal Detection of Ferromagnetic Pipeline

Variational Specific Mode Extraction: A Novel Method for Defect Signal Detection of Ferromagnetic Pipeline

Random field-aided tracking of autonomous kinetically passive wireless agents

Microwave-Based SAR Technique for Pipeline Inspection Using Autofocus Range-Doppler Algorithm

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