Investigating the Relative Severity of Dents in Pipelines Based on Magnetic Flux Leakage Inspection Data

Author(s):  
Leanne M. Tindall ◽  
Julia M. Race ◽  
Jane Dawson

Dent damage in pipelines may result from either impact damage caused by third parties or construction damage. Third party damage generally occurs on the upper half of the pipe (between the 8 o’clock and 4 o’clock positions) and has historically contributed to the highest number of pipeline failures. Dents caused during construction generally occur on the bottom half of the pipe and tend to be constrained by the indenter causing the dent, i.e. a stone or rock in the pipeline bed/backfill. However, all dents have the potential to cause an increase in stress in the pipeline, and consequently increase the pipeline sensitivity to both static and fatigue loading. Although there are extensive recommendations for the ranking and repair of dents, recently, failures of dents that are acceptable to pipeline codes have been reported. Guidance is therefore needed in order that operators can identify dents for which excavation and inspection is uneconomic and could potentially be damaging to pipeline safety and dents for which further action is required. This paper provides a review of the published recommendations for the treatment of pipeline dents and goes on to present a method that is being developed to determine the relative severity of dents in a pipeline using magnetic flux leakage (MFL) signal data. The proposed method involves measuring MFL signal parameters related to the geometry of the dent and relating these to high resolution caliper inspection data. This analysis enables a relationship to be established between the MFL signal data and dent depth and shape measurements. Once the model is verified, this analysis can then be used to provide a severity ranking for dents on pipelines where only MFL data is available.

2017 ◽  
Vol 898 ◽  
pp. 1069-1078
Author(s):  
Ning Qiao ◽  
Mu Xiao Shan ◽  
Ye Zheng Li

To investigate the influence of stress concentration, crack propagation and types of fatigue loading on metal magnetic memory signals, two groups of fatigue experiments with different types of fatigue loading were carried out on Q235B steel welded joint. The normal components of magnetic flux leakage were measured by metal magnetic memory tester in the course of fatigue test, and the fracture surfaces of specimens were observed by scanning electron microscopy after fatigue tests. The experimental results showed that the normal components of magnetic flux leakage filed, as well as the metal magnetic memory signal, changed polarity and their gradients have peak values at stress concentration zones. The zero position of the normal component of magnetic flux leakage changed gradually with increasing cycle numbers. In addition, the metal magnetic memory signal feature of fatigue crack propagation was affected by the loading type clearly. Moreover, a combination of brittle rupture and ductile rupture was obtained in the fracture morphology figure.


Electronics ◽  
2021 ◽  
Vol 10 (12) ◽  
pp. 1436
Author(s):  
Tuoru Li ◽  
Senxiang Lu ◽  
Enjie Xu

The internal detector in a pipeline needs to use the ground marker to record the elapsed time for accurate positioning. Most existing ground markers use the magnetic flux leakage testing principle to detect whether the internal detector passes. However, this paper uses the method of detecting vibration signals to track and locate the internal detector. The Variational Mode Decomposition (VMD) algorithm is used to extract features, which solves the defect of large noise and many disturbances of vibration signals. In this way, the detection range is expanded, and some non-magnetic flux leakage internal detectors can also be located. Firstly, the extracted vibration signals are denoised by the VMD algorithm, then kurtosis value and power value are extracted from the intrinsic mode functions (IMFs) to form feature vectors, and finally the feature vectors are input into random forest and Multilayer Perceptron (MLP) for classification. Experimental research shows that the method designed in this paper, which combines VMD with a machine learning classifier, can effectively use vibration signals to locate the internal detector and has the characteristics of high accuracy and good adaptability.


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