Magnetic flux leakage (MFL) detection is one of the most popular methods of pipeline inspection. It is a nondestructive testing technique which uses magnetic sensitive sensors to detect the magnetic leakage field of defects on both the internal and external surfaces of pipelines. This paper introduces the main principles, measurement and processing of MFL data. As the key point of a quantitative analysis of MFL detection, the identification of the leakage magnetic signal is also discussed. In addition, the advantages and disadvantages of different identification methods are analyzed. Then the paper briefly introduces the expert systems used. At the end of this paper, future developments in pipeline MFL detection are predicted.

https://www.mdpi.com/1424-8220/15/12/29845/pdf

Theory and Application of Magnetic Flux Leakage Pipeline Detection

This article proposes a hybrid intelligent-classic control approach for reconstruction and compensation of cyber attacks launched on inputs of nonlinear cyber-physical systems (CPS) and industrial Internet of Things systems, which work through shared communication networks. In this article, a class of n -order nonlinear systems is considered as a model of CPS while it is in presence of cyber attacks only in the forward channel. An intelligent-classic control system is developed to compensate cyber-attacks. Neural network (NN) is designed as an intelligent estimator for attack estimation and a classic nonlinear control system based on the variable structure control method is designed to compensate the effect of attacks and control the system performance in tracking applications. In the proposed strategy, nonlinear control theory is applied to guarantee the stability of the system when attacks happen. In this strategy, a Gaussian radial basis function NN is used for online estimation and reconstruction of cyber-attacks launched on the networked system. An adaptation law of the intelligent estimator is derived from a Lyapunov function. Simulation results demonstrate the validity and feasibility of the proposed strategy in car cruise control application as the testbed.

Artificial Intelligence for Detection, Estimation, and Compensation of Malicious Attacks in Nonlinear Cyber-Physical Systems and Industrial IoT

In most current nondestructive testing systems, a magnetic flux leakage (MFL) method is widely used in various industry fields, where the structural integrity of specimens is of vital importance. The estimation of defect size in specimen from the MFL measurements is a key and difficult problem. The traditional methods have low precision because feature extraction procedure relies on prior knowledge and the ability of designer. Inspired by the idea of convolutional neural network (CNN), a novel visual transformation CNN (VT-CNN) is proposed in this paper to overcome the limitation of traditional method in a feature extraction procedure. By adding a visual transformation layer according to the characteristics of the MFL measurements, the VT-CNN can distinguish the defect feature with different sizes more accurately. Moreover, since the VT-CNN method is designed based on the deep learning theory, more industrial big data with accurate label should be used to train the network. Due to the difficulty of making real industrial big data, a novel mesher magnetic dipole model is designed to simulate this industrial process. A large simulated MFL measurement of irregular defects produced by this model can increase the number of training samples and improve the robustness of the network. Experiments to estimate natural corrosion defects on real industrial pipelines are performed to validate the proposed framework. The experimental results are illustrated in detail, which highlights the superiority of the proposed method in industrial applications.

An Estimation Method of Defect Size From MFL Image Using Visual Transformation Convolutional Neural Network

Oil and gas pipelines transport and distribute large quantities of oil products and natural gas to industrial and residential customers over a long distance. However, pipeline failures could lead to enormous hazards and safety issues. Metal loss, due to corrosion, is a significant cause for pipeline failures. To detect and quantify metal loss, in-line inspection (ILI) is carried out periodically to assess the integrity of the pipeline. Among all the ILI techniques, magnetic-flux leakage (MFL) is the most popular one due to its high efficiency, robustness, and good applicability to both oil and gas pipelines. This article provides a comprehensive review of the pipeline corrosion assessment with the MFL technique from the data analytic perspective. The analyses of the MFL signal and data contribute to both corrosion quantification and prediction. In this article, the state of the art for the MFL measurement technique is briefly reviewed first. For corrosion quantification, the signal processing methods together with the characterization models, which aim to enhance the measurement and characterize the corrosion, are described and discussed. For corrosion prediction, this article investigates multiple MFL data matching methods, which align defects from successive ILI runs. Subsequently, corrosion growth models, which aim to predict the future corrosion status, are presented. Besides, the reliability analysis of the corroded pipeline is reviewed. The potential of fusing MFL with other non-destructive testing (NDT) techniques are explored as well. At the end of this article, we summarize the existing issues and describe the trends for future research on pipeline corrosion assessment.

Analysis of Magnetic-Flux Leakage (MFL) Data for Pipeline Corrosion Assessment

This paper reports various chaotic phenomena that occur in a single-link flexible joint (SLFJ) robot manipulator. Four different cases along with subcases are considered here to show different types of chaotic behaviour in a flexible manipulator dynamics. In the first three cases, a partial state feedback as joint velocity and motor rotor velocity feedback is considered, and the resultant autonomous dynamics is considered for analyses. In the fourth case, the manipulator dynamics is considered as a non-autonomous system. The system has (1) one stable spiral and one saddle-node foci, (2) two saddle-node foci and (3) one marginally stable nature of equilibrium points. We found single- and multi-scroll chaotic orbits in these cases. However, with the motor rotor velocity feedback, the system has two unstable equilibria. One of them has an index-4 spiral repellor. In the non-autonomous case, the SLFJ robot manipulator system has an inverse crisis route to chaos and exhibits (1) transient chaos with a stable limit cycle and (2) chaotic behaviour. In all the four cases, the SLFJ manipulator dynamics exhibits coexistence of chaotic orbits, i.e. multi-stability. The various dynamical behaviours of the system are analysed using available methods like phase portrait, Lyapunov spectrum, instantaneous phase plot, Poincare map, parameter space, bifurcation diagram, 0–1 test and frequency spectrum plot. The MATLAB simulation results support various claims made about the system. These claims are further confirmed and validated by circuit implementation using NI Multisim.

Coexistence of single- and multi-scroll chaotic orbits in a single-link flexible joint robot manipulator with stable spiral and index-4 spiral repellor types of equilibria

Magnetic flux leakage (MFL) method is the most widely used non-destructive testing techniques for detection and characterization of defects in product transmission pipelines. The maximum safe operating pressure is a crucial parameter in practice, which is predicted with respect to the length, width, and depth of defects. In a previous work, an algorithm is devised to detect and estimate the width of the defect based on MFL signals. In this paper, an efficient method based on axial MFL level contours is devised to estimate the length of the defect. This method uses the patterns of signal level contours in the region corresponding to the defect’s area. In addition, a Gaussian radial basis function neural network (NN) is trained to approximate the depth of the defect. The NN is fed with the estimated length, width, and signal peak-to-peak values, and the output of the network is the estimated depth. The proposed detection and estimation method is applied to MFL measurements to detect and determine the sizing of metal loss defects. The efficacy and accuracy of the proposed methods are examined through a rich set of sample defects that contains simulated defects, designed defects that are carved on a real pipe by milling, and actual pigging data validated by several dig up verifications. Obtained results confirm the effectiveness and accuracy of the proposed depth and length estimation method along with the previously devised detection and width estimation methods, in characterization of metal loss defects.

Estimation of Depth and Length of Defects From Magnetic Flux Leakage Measurements: Verification With Simulations, Experiments, and Pigging Data

This paper presents a variable structure control and anti control for trajectory tracking and vibration control of a flexible joint manipulator. To study the effectiveness of the controllers, designed controller is developed for tip angular position control of a flexible joint manipulator. Based on Lyapunov stability theory for variable structure control, the nonlinear controller and some generic sufficient conditions for global asymptotic control are attained. Also in this study, the anti-control is applied to reduce the deflection angle of flexible joint system. To achieve this goal, the chaos dynamic must be created in the flexible joint system. So, the flexible joint system has been synchronized to chaotic gyroscope system. In this study, control and anti-control concepts are applied to achieve the high quality performance of flexible joint system. It is tried to design a controller which is capable to satisfy the control and anti- control aims. The performances of the proposed control are examined in terms of input tracking capability, level of vibration reduction, and time response specifications. Finally, the efficacy of the proposed method is validated through experimentation on QUANSER's flexible-joint manipulator.

Variable structure control and anti-control of flexible joint manipulator with experimental validation

Magnetic Flux Leakage (MFL) testing is the most widely used non-destructive techniques for the in-service inspection of oil and gas pipelines. This study presents novel approaches to detect defects by employing MFL signals. Estimating the number of defects, locations, and orientations from measurements is a typical inverse problem in electromagnetic non-destructive evaluations (NDE). A detection algorithm on axial flux is proposed for defect detection based on image processing approaches and morphological methods. Finally, the efficacy and accuracy of the proposed algorithm is validated through examinations on simulated defects and real experimental MFL data. Simulated defects are generated in presence of multiple uncertainties and noises; including the variation of the pipeline thickness, sensors liftoff, magnetization level, and shape irregularity.

Detection of natural gas pipeline defects using magnetic flux leakage measurements

Magnetic Flux Leakage (MFL) testing is the most widely used non-destructive techniques for the in-service inspection of oil and gas pipelines. In this study, a novel approach for detecting and estimating the width of defects by employing MFL signals is presented. Estimating the locations of defects and profiles of lengths, widths, and depths of defects from measurements is a typical inverse problem in electromagnetic non-destructive testing. In this study, defect parameters are estimated in two separate consecutive steps. In the first step, a detection algorithm based on image processing approaches is applied on axial flux to estimate the numbers of defects, locations, and orientations of defects. Then, to estimate widths of defects, an inversion procedure based on 2D signal processing is applied on radial flux corresponding to areas detected in previous step. Finally, the efficacy and accuracy of the proposed algorithm is validated through examinations on simulated defects and real experimental MFL data. Simulated defects are generated in presence of multiple uncertainties and noises.

Defect detection and width estimation in natural gas pipelines using MFL signals

A novel structure of fuzzy logic controller is presented for trajectory tracking and vibration control of a flexible joint manipulator. The rule base of fuzzy controller is divided into two sections. Each section includes two variables. The variables of first section are the error of tip angular position and the error of deflection angle, while the variables of second section are derivatives of mentioned errors. Using these structures, it would be possible to reduce the number of rules. Advantages of proposed fuzzy logic are low computational complexity, high interpretability of rules, and convenience in fuzzy controller. Implementing of the fuzzy logic controller on Quanser flexible joint reveals efficiency of proposed controller. To show the efficiency of this method, the results are compared with LQR method. In this paper, experimental validation of proposed method is presented.

/pdf/control-of-flexible-joint-manipulator-via-reduced-rule-based-4sa024ac8h.pdf

Mojtaba Rostami Kandroodi

Papers

Estimation of Depth and Length of Defects From Magnetic Flux Leakage Measurements: Verification With Simulations, Experiments, and Pigging Data

Variable structure control and anti-control of flexible joint manipulator with experimental validation

Detection of natural gas pipeline defects using magnetic flux leakage measurements

Defect detection and width estimation in natural gas pipelines using MFL signals

Control of Flexible Joint Manipulator via Reduced Rule-Based Fuzzy Control with Experimental Validation