The magnetic method for in-tube nondestructive testing of gas and oil pipelines: The past and the present

Loskutov, V. E.; Matvienko, A. F.; Patramanskii, B. V.; Shcherbinin, V. E.

doi:10.1134/s1061830906080018

Cited by 27 publications

(11 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In general, the magnetic flux leakage (MFL) inspection service of pipelines could help to acquire information on the position of any detected defects and the extent/degree of pipeline damage due to such defects [1] . The information can be used for pipeline safety risk assessment and the development of a pipeline repair programme; however, it would also be desirable to report whether the defects are on the internal or external surfaces of the pipelines, which is the focus of this paper.…”

Section: Introductionmentioning

confidence: 99%

Internal and external defect identification of pipelines using the PSO-SVM method

Jian¹,

Ai²

2015

Insight

View full text Add to dashboard Cite

Internal and external defect identification of pipelines using the PSO-SVM method Defects due to corrosion can occur on both the internal and external surfaces of a pipeline. Magnetic flux leakage (MFL) pipeline inspection gauges can detect and locate defects on both the internal and external wall surfaces of pipelines but are generally unable to discriminate between internal and external defects. Therefore, this paper presents a classification approach to achieve defect discrimination based on support vector machines (SVM). First, time-frequency analysis is employed to acquire the feature vectors of the time domain and frequency domain from the MFL signal. Then, a distance evaluation technique is used to remove the redundant and irrelevant information and select the salient features for the classification process. Next, the selected feature vectors are used as the inputs to the proposed SVM model of defect classification. The parameters of this model are optimised using the particle swarm optimisation (PSO) algorithm. Finally, with the optimised model parameters and training sample data, the classification model of the location of the defect based on SVM is established. The experimental results indicate that the proposed method is an effective approach for internal and external defect classification based on the MFL signal.

show abstract

Section: Introductionmentioning

confidence: 99%

Internal and external defect identification of pipelines using the PSO-SVM method

Jian¹,

Ai²

2015

Insight

View full text Add to dashboard Cite

show abstract

“…Chaotic time series theory can indicate the nonlinear characteristics of complex system by a single variable time series data, and also has the ability to detect weak signals [7][8]. Therefore, 6 typical pressure signals in the same pipe section are selected as the analysis objects in this paper.…”

Section: Introductionmentioning

confidence: 99%

Nonlinear Analysis Chaotic Characteristics of Leak Signal of Pipeline based on Reconstructed Phase Space

Shi¹,

Xu²,

Qu³

et al. 2015

IJSEIA

View full text Add to dashboard Cite

The In this paper, nonlinear analysis is used to study the possibility of chaotic behavior of pipeline pressure signal. Based on real-time data of pipeline leak monitoring system, oil pipeline pressure signals have been verified to be chao system by analyzing the chaotic characteristic of pressure time series. Six typical measured data of pipeline pressure are selected and reconstructed to the higher phase space. And then the largest Lyapunov exponent of each data is calculated to test and verify the chaotic characteristics of the pressure signal. For pipeline leakage fault diagnosis, the approximate entropy (ApEn) has been applied to extract the nonlinear and chaotic characteristics. By calculating the ApEn of normal, operation and leakage signals, the results indicate that the value ranges of three kinds of signals are above 0.35, below 0.025, and from 0.025 to 0.35. And the identification rate of pipeline leakage has been reached 90.0% only based on ApEn, which provide more effective basis for the classification and identification of chaotic characteristics of the pressure signal.

show abstract

“…Thus, MFL testing is based on detecting the magnetic field that leaks from a pipe's wall in the vicinity of surface and subsurface flaws. See [1]- [4] for reviews of history, problems and concerns.…”

Section: Introductionmentioning

confidence: 99%

“…Of special interest to pipeline operators is the extent and location of various defects that can adversely affect the integrity of the pipeline and its operation. Some of these defects include corrosion 1 , deformations, fatigue, hairline cracks, dents, buckles, de-laminations, and faulty welds. The results are used to determine repair and replacement priorities for a pipeline operator.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Techniques for the Analysis of Magnetic Flux Leakage Images in Pipeline Inspection

et al. 2009

View full text Add to dashboard Cite

The magnetic flux leakage (MFL) technique is commonly used for non-destructive testing of oil and gas pipelines. This testing involves the detection of defects and anomalies in the pipe wall, and the evaluation of the severity of these defects. The difficulty with the MFL method is the extent and complexity of the analysis of the MFL images. In this paper we show how modern machine learning techniques can be used to considerable advantage in this respect. We apply the methods of support vector regression, kernelization techniques, principal component analysis, partial least squares, and methods for reducing the dimensionality of the feature space. We demonstrate the adequacy of the performance of these methods using real MFL data collected from pipelines, with regard to the performance of both the detection of defects, and the accuracy in the estimation of the severity of the defects. We also show how low-dimensional latent variable structures can be effective for visualizing the clustering behaviour of the classifier.

show abstract

The magnetic method for in-tube nondestructive testing of gas and oil pipelines: The past and the present

Cited by 27 publications

References 0 publications

Internal and external defect identification of pipelines using the PSO-SVM method

Internal and external defect identification of pipelines using the PSO-SVM method

Nonlinear Analysis Chaotic Characteristics of Leak Signal of Pipeline based on Reconstructed Phase Space

Machine Learning Techniques for the Analysis of Magnetic Flux Leakage Images in Pipeline Inspection

Contact Info

Product

Resources

About