The evaluation of the bouncing model of caliper pig’s detection arm in subsea gas pipelines

2018 ◽  
Vol 233 (3) ◽  
pp. 735-744
Author(s):  
Xiaolong Li ◽  
Renyang He ◽  
Tao Meng ◽  
Shimin Zhang
Keyword(s):  
Theoretical Model ◽  
Magnetic Flux ◽  
Experimental Method ◽  
Model Calculation ◽  
Ultrasonic Test ◽  
Magnetic Flux Leakage ◽  
Transport Capacity ◽  
Gas Pipelines ◽  
Deformation Defect ◽  
Flux Leakage

Caliper pigs are widely used for measuring pipeline internal geometry and detecting anomalies, such as dents, wrinkles and flat spots. Due to its excellent transport capacity, caliper pig is more widely used in subsea gas pipelines than smart pig, such as Magnetic Flux Leakage (MFL) pig or Ultrasonic Test (UT) pig. In this article, the bouncing process of detection arm of caliper pig across convex defect is studied. And then, the influence of the collision between the detection arm and deformation defect, as well as the defect shape on the bounce, is discussed. Based on that, a bouncing theoretical model of the detection arm is developed for analyzing the bouncing phenomenon. Furthermore,the bouncing process that the detection arm moves across the convex defect with different velocities and different initial spring forces is studied by experimental method. The bouncing model calculation value is approximately equal with the experimental value, verifying the validity of the bouncing model. The bouncing model has a great significance for caliper pig evaluating the pipeline convex defect.

scholarly journals Location Detection Method of Detector in Pipeline Using VMD Algorithm and Machine Learning Classifier

Electronics ◽  
2021 ◽  
Vol 10 (12) ◽  
pp. 1436
Author(s):  
Tuoru Li ◽  
Senxiang Lu ◽  
Enjie Xu
Keyword(s):  
Machine Learning ◽  
Magnetic Flux ◽  
Magnetic Flux Leakage ◽  
Intrinsic Mode Functions ◽  
Detection Range ◽  
Vibration Signals ◽  
Feature Vectors ◽  
Learning Classifier ◽  
Mode Decomposition ◽  
Flux Leakage

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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