In-line Inspection Method Research Articles

A novel stress concentration inspection method for marine oil and gas pipeline based on UNSM

Stress concentration of marine oil and gas pipelines can lead to deformation and cracks, laying out great hidden dangers for safe operation. This paper proposes a novel in-line inspection method for stress concentration based on UNSM (unsaturated magnetization) for oil and gas pipelines. Firstly, the stress concentration inspection mechanism with UNSM is analyzed, and the equivalent magnetic circuit model of the inspection probe is derived. Secondly, the response signal features of axial, radial and complex stresses were investigated parametrically by numerical simulation. Then, quantitative tensile stress inspection was carried out with the UNSM inspection probe. Finally, the pipeline stress state is continuously scanned by the self-developed defect dynamic scanning experimental system. The results show that the UNSM inspection probe can effectively detect the abnormal stress of the pipeline. Meanwhile, the axial tensile, radial compressive stress and complex stress increase the axial permeability of the pipeline, resulting in a negative correlation of characteristic signals. Besides, the signal feature shows the "basin" distribution and decreases gradually with the increase of the lift-off. The above research provides a reference for developing in-line inspection equipment for the abnormal stress of marine pipelines.

A Decade Review of the Art of Inspection and Monitoring Technologies for Long-Distance Oil and Gas Pipelines in Permafrost Areas

Long-distance oil and gas pipelines buried in permafrost areas will inevitably encounter typical geological disasters, such as frost heave and thaw settlement and sliding, which easily cause pipeline displacement, bending, or deformation. When there are certain defects in the pipeline, additional complex, external stress will further lead to the failure of the pipeline or weld and can even lead to serious accidents such as pipeline leakage, pipe burst, or fracture. This paper introduces in detail the typical defects and risks of buried pipelines in permafrost areas and summarizes the in-line inspection technologies, off-line inspection technologies, and integrated monitoring systems for pipelines in the pipeline industry. Regarding pipelines in permafrost areas, in-line inspection methods may be employed. These include magnetic flux leakage, electromagnetic eddy current, ultrasonic, IMU, and electromagnetic acoustic transducer inspections. Off-line inspection is also one of the important means of inspecting a pipeline in a permafrost area. Indirect inspection is combined with verification by direct inspection to check and evaluate the integrity of the anticorrosive coating and the effectiveness of the cathodic protection for the pipeline. Meanwhile, considering the external environment of a pipeline in a permafrost area, a monitoring system should be developed and established. This paper discusses and projects the future development of related technologies, which provides reference for the construction and operation of pipelines in permafrost areas.

A Discrete Partially Observable Markov Decision Process Model for the Maintenance Optimization of Oil and Gas Pipelines

Corrosion is one of the major causes of failure in pipelines for transporting oil and gas products. To mitigate the impact of this problem, organizations perform different maintenance operations, including detecting corrosion, determining corrosion growth, and implementing optimal maintenance policies. This paper proposes a partially observable Markov decision process (POMDP) model for optimizing maintenance based on the corrosion progress, which is monitored by an inline inspection to assess the extent of pipeline corrosion. The states are defined by dividing the deterioration range equally, whereas the actions are determined based on the specific states and pipeline attributes. Monte Carlo simulation and a pure birth Markov process method are used for computing the transition matrix. The cost of maintenance and failure are considered when calculating the rewards. The inline inspection methods and tool measurement errors may cause reading distortion, which is used to formulate the observations and the observation function. The model is demonstrated with two numerical examples constructed based on problems and parameters in the literature. The result shows that the proposed model performs well with the added advantage of integrating measurement errors and recommending actions for multiple-state situations. Overall, this discrete model can serve the maintenance decision-making process by better representing the stochastic features.

Bimorph sensor based in-line inspection method for corrosion defect detection in natural gas pipelines

Recently, bimorph sensors based on the in-line inspection (ILI) method have shown considerable promise for detecting surface defects (e.g., corrosions) in gas pipelines. However, the effect of physical parameters such as bimorph position and scanning speed on the performance of the bimorph sensor has not yet been investigated. As a result, the capability of the bimorph sensor for defect detection in pipelines is not clear. This study aims to optimize the physical parameters and investigate the influence of physical parameters on the performance of the bimorph sensor. For this purpose, first various corrosion defect sizes were considered and analyzed using piezoelectric theory. Next, an analytical relationship between the vibration response of the bimorph sensor and defect is derived using the Euler-Bernoulli beam theory. Finally, a real-time field test was conducted based on the installation of the proposed method on a developed smart PIG to demonstrate the detection efficiency of the bimorph sensor. The novel contributions of this study are as follows: (1) The proposed analytical method uses cantilever beam vibrational mode shapes together with the piezoelectric effect to detect and quantify the corrosion defects. (2) The current study proposed a bimorph sensor method capable of performing high scanning speed (0.5 m/s) in real-time pipeline inspection. (3) The performance of the proposed method on the various position of bimorph on the cantilever probe and scanning speed is studied analytically and experimentally. (4) Multiple corrosion defects and pipeline joints were detected and localized successfully in complex pipeline networks. • Proposed a bimorph sensor method to detect corrosion defects in gas pipelines. • Performance of the proposed method is investigated analytically and experimentally. • Bimorph sensor is designed and tested in a laboratory testbed. • Effectiveness of the proposed method on various physical parameters is presented. • Field test is conducted with the designed bimorph sensor array.

Image Classification Method Based on Improved Deep Convolutional Neural Networks for the Magnetic Flux Leakage (MFL) Signal of Girth Welds in Long-Distance Pipelines

Girth weld defects in long-distance oil and gas pipelines are one of the main causes of pipeline leakage failure and serious accidents. Magnetic flux leakage (MFL) is one of the most widely used inline inspection methods for long-distance pipelines. However, it is impossible to determine the type of girth weld defect via traditional manual analysis due to the complexity of the MFL signal. Therefore, an automatic image classification method based on deep convolutional neural networks was proposed to effectively classify girth weld defects via MFL signals. Firstly, the image data set of girth welds MFL signal was established with the radiographic testing results as labels. Then, the deep convolutional generative adversarial network (DCGAN) data enhancement algorithm was proposed to enhance the data set, and the residual network (ResNet-50) was proposed to address the challenge presented by the automatic classification of the image sets. The data set after data enhancement was randomly selected to train and test the improved residual network (ResNet-50), with the ten validation results exhibiting an accuracy of over 80%. The results indicated that the improved network model displayed a strong generalization ability and robustness and could achieve a more accurate MFL image classification of the pipeline girth welds.

Discriminative Method for Crack Detection Signals in Balanced-Field Electromagnetic Technique Based on Amplitude-Phase Composite Figure.

The balanced-field electromagnetic technique is an effective in-line inspection method for pipeline cracks. To address the problem that the interference signal generated by the tilt jitter of the sensor during the detection process affects the judgment of cracks, this paper proposes a method to differentiate the crack detection signal from the sensor jitter signal by using an amplitude-phase composite figure. The generation principle of the detection signal was analyzed by using the mutual inductance model, and the amplitude-phase composite figure was constructed by using the components of the detection signal after quadrature demodulation. The feasibility of using the phase as a signal discrimination feature was illustrated by finite element simulations, and the characteristics of the amplitude-phase composite figure were determined. The validity of the proposed method was verified experimentally. The results show that the crack detection signal and the signal generated by the sensor jitter are of the same frequency with similar waveforms and significantly different phases. The phase base value of the crack detection signal ranges from 35° to 55°, and the phase base value of the jitter signal is −4°. In terms of the characteristics of the amplitude-phase composite figure, the crack detection signal distribution is symmetrical about the origin in the first and third quadrants, and the axial crack is closer to the Y-axis than the circumferential crack; the jitter signal is distributed in the second and fourth quadrants and has a very small angle to the X-axis. In addition, the proposed method effectively weakens the observation of the phase noise region in the detection signal of the balanced-field electromagnetic technique.

Investigation of an Inline Inspection Method for the Examination of Cylinder-like Specular Surfaces Using Deflectometry

An optical measuring method is presented, with which it is possible to measure and evaluate reflective cylindrical surfaces using a combination of deflectometry and subsequent reconstruction of the surface. The system is set up and tested on rolling elements of cylindrical roller bearings. However, it is not limited to this use case and can be applied to other cylindrical specular surfaces. The system distinguishes itself from existing test methods through the combination of high-resolution three-dimensional defect measurement with a very short recording time, and offers the possibility of introducing tolerance limits in the production of cylindrical specular surfaces. With this method, it is possible to record a defect with the dimensions of 1.3mm by 1.8mm within 5 s and to reconstruct the absolute depth. The resolution of the system is below 10 μm in both X- and Y-direction, and is therefore sufficiently accurate to detect typical surface defects such as scratches, dents, or deformations. To validate the measured values of the system, the results of an artificially generated 10.35 μm deep defect location were compared with those of a highly accurate mechanical stylus measurement.

A Novel Feature Identification Method of Pipeline In-Line Inspected Bending Strain Based on Optimized Deep Belief Network Model

Both long-distance oil and gas pipelines often pass through areas with unstable geological conditions or natural disasters. As a result, they are prone to bending, displacement, and deformation due to the action of an external environmental loading, which poses a threat to the safe operation of pipelines. The in-line inspection method that is based on the implementation of high-precision inertial measurement units (IMU) has become the main means of pipeline bending stress-strain detection technique. However, to address the problems of the inconsistent identification, low identification efficiency, and high misjudgment rate during the application of the traditional manual identification methods, a feature identification approach for the in-line inspected pipeline bending strain based on the employment of an optimized deep belief network (DBN) model is proposed in this work. In addition, our model can automatically learn features from the pipeline bending strain signals and complete classification and identification. On top of that, after the network model was trained and tested by using the actual pipeline bending strain inspection data, the extracted results showed that the model after the implementation of the training process could accurately identify and classify various pipeline features, with an identification accuracy and efficiency of 97.8% and 0.02 min/km, respectively. The high efficiency, elevated accuracy, and strong robustness of our method can effectively improve the in-line inspection procedure of pipelines during the enforcement of a bending strain load.

Influence of Baking Temperature and Formulation on Physical, Sensorial, and Morphological Properties of Pogácsa Cake: An Image Analysis Study.

Pogácsa is a high-demand bakery product with a unique texture, where crumb structure is a determining factor for its textural quality and consumer acceptability. At present, there is no non-destructive in-line inspection method for textural quality assessment of pogácsa. Therefore, this study was aimed to evaluate the texture of pogácsa using the image processing technique, which was prepared using different cheeses with varying moisture contents (MC) and was baked at 200 and 215 °C. Samples were assessed for textural, visual, physical, and sensorial properties. The findings indicated that the highest porosity (72.75%) was found for the sample baked at 215 °C with low-moisture cheese (58%), while the lowest porosity (32.66%) was observed for cheese-free sample baked at 200 °C. Pore volumetric ratio and MC showed strong correlations (p < 0.01) with hardness (−0.90 and −0.89), resilience (0.87 and 0.83), cohesiveness (0.84 and 0.82), springiness (0.87 and 0.90), gumminess (−0.92 and −0.92), and chewiness (−0.92 and −0.92), respectively. The pore volumetric ratio showed a strong correlation (p < 0.01) with reference porosity (0.71). Overall, the current study indicated that adding cheese with varying MC and baking temperature could affect the texture of pogácsa cake, which could be detected by image analysis.

A Review: Application of Terahertz Nondestructive Testing Technology in Electrical Insulation Materials

As the voltage level of the grid continuously rises, more and more new insulating materials with better performance are put into use. Nevertheless, air gaps often appear in insulating materials due to poor material quality, structural design flaws, etc. High-field-strength air gaps will produce partial discharge or even breakdown, resulting in electrical equipment failure. In addition, during operation, organic insulating materials are simultaneously exposed to high temperatures, mechanical stress, electrical stress, and moisture. Consequently, aging is inevitable. The aging of insulation materials is the root cause of the reduced insulation properties of the degradation of the insulation properties of electrical equipment in long-term operation. In order to improve the safety performance of power equipment and assure the safety of power grids, the detection and identification of internal structural defects and aging states of insulating materials have always been hot issues in related fields. However, structural defects inside the insulation materials used in power equipment, which are tiny (microns to millimeters), are exceedingly challenging to detect. While there are various destructive methods for detecting the aging status, non-destructive in-line inspection methods are still less commonly reported. In recent years, some scholars have begun to try to use terahertz spectroscopy to detect insulating materials. Numerous scholars have discovered that terahertz spectroscopy can not only detect structural defects inside insulating materials but can also detect non-destructive testing on the degree of material aging and even the type of material, which has excellent engineering value and promotion prospects. In order to help relevant researchers understand the application of this emerging technology in the field of insulating materials, this paper reviews the basic principles of terahertz detection and its applications in air gap detection, moisture detection, and aging analysis of insulating materials. Existing problems and future development directions are discussed.

Pipeline In-Line Inspection Method, Instrumentation and Data Management.

Pipelines play an important role in the national/international transportation of natural gas, petroleum products, and other energy resources. Pipelines are set up in different environments and consequently suffer various damage challenges, such as environmental electrochemical reaction, welding defects, and external force damage, etc. Defects like metal loss, pitting, and cracks destroy the pipeline’s integrity and cause serious safety issues. This should be prevented before it occurs to ensure the safe operation of the pipeline. In recent years, different non-destructive testing (NDT) methods have been developed for in-line pipeline inspection. These are magnetic flux leakage (MFL) testing, ultrasonic testing (UT), electromagnetic acoustic technology (EMAT), eddy current testing (EC). Single modality or different kinds of integrated NDT system named Pipeline Inspection Gauge (PIG) or un-piggable robotic inspection systems have been developed. Moreover, data management in conjunction with historic data for condition-based pipeline maintenance becomes important as well. In this study, various inspection methods in association with non-destructive testing are investigated. The state of the art of PIGs, un-piggable robots, as well as instrumental applications, are systematically compared. Furthermore, data models and management are utilized for defect quantification, classification, failure prediction and maintenance. Finally, the challenges, problems, and development trends of pipeline inspection as well as data management are derived and discussed.

An innovative approach towards defect detection and localization in gas pipelines using integrated in-line inspection methods

The current study presents a novel and high-sensitivity in-line inspection (ILI) method for gas pipelines. The proposed method is based on integrated ILI methods, optical sensor and bimorph sensor. The uniqueness of the proposed method lies in; (1) combining two advanced methods for more accurate and sensitive outcome, (2) real-time inspection of target pipeline, (3) specifically designed sensor array to estimate and locate defects in three dimensions, and (4) low power consumption and economic viability. The theoretical framework for the relationship between the sensor outputs and the presence of defect as well as the design of the sensor housing module are also developed and presented. Laboratory tests are conducted on various types of defects to illustrate the robustness and sensitivity of the method. Additionally, the conventional magnetic flux leakage (MFL) method is adopted to verify the effectiveness of the proposed method. Furthermore, for the real-world application purposes, a smart pipeline inspection gauge (smart PIG) with integrated different sensor arrays are developed for the field testing of gas pipeline networks. The two-dimensional (2-D) image produced by the sensor array is presented to visualize the inner surface of the pipeline and enable accurate identification of bolted pipe joints. A speed control system is also developed and designed to eliminate errors caused by speed spikes and maintain the speed of the smart PIG within the desired range. An analysis of the chatter vibration signal of the smart PIG running in a gas pipeline network is presented. The current research work provides a solid base towards a step change from a pilot study to a real-time, real-world application of a novel inspection method for pipelines.

A Real-Time, Non-Contact Method for In-Line Inspection of Oil and Gas Pipelines Using Optical Sensor Array.

Corrosion is considered as one of the most predominant causes of pipeline failures in the oil and gas industry and normally cannot be easily detected at the inner surface of pipelines without service disruption. The real-time inspection of oil and gas pipelines is extremely vital to mitigate accidents and maintenance cost as well as to improve the oil and gas transport efficiency. In this paper, a new, non-contact optical sensor array method for real-time inspection and non-destructive evaluation (NDE) of pipelines is presented. The proposed optical method consists of light emitting diodes (LEDs) and light dependent resistors (LDRs) to send light and receive reflected light from the inner surface of pipelines. The uniqueness of the proposed method lies in its accurate detection as well as its localization of corrosion defects, based on the utilization of optical sensor array in the pipeline, and also the flexibility with which this system can be adopted for pipelines with different services, sizes, and materials, as well as the method’s economic viability. Experimental studies are conducted considering corrosion defects with different features and dimensions to confirm the robustness and accuracy of the method. The obtained data are processed with discrete wavelet transform (DWT) for noise cancelation and feature extraction. The estimated sizes of the corrosion defects for different physical parameters, such as inspection speed and lift-off distance, are investigated and, finally, some preliminary tests are conducted based on the implementation of the proposed method on an in-line developed smart pipeline inspection gauge (PIG) for in-line inspection (ILI) application, with resulting success.

Research on Strain and Displacement ILI Technology for Oil &amp; Gas Pipeline

The safe and rapid transportation of oil and natural gas, as strategic resources for national energy and power, is one of the important factors restricting the development of national economy. The pipeline often passes some areas vulnerable to geological hazards, placing pipes under the bending stress caused by bending strain apart from normal internal pressure load. Hence, the existence of bending strain on pipeline severely affects the structural integrity and operational safety of pipeline. Especially when any severe defect exists at the position with flexural strain, it may cause the failure of pipeline more easily. This paper proposes an In-line inspection method for pipeline displacement and strain which based on inertial navigation technology. The measurement of pipeline centerline and algorithm of pipeline strain are presented. The method has been verified by pull through and field test. It offers an effective method for safe operation of long-distance oil & gas transportation pipeline in seismic area.

Robust defect detection method for a non-periodic TFT-LCD pad area

In this work, we propose an effective inline inspection method for non-repeating patterns on TFT-LCDs which can be quickly applied to current manufacturing processes and easily automated. Given that the TFT-LCD active area is composed of regularly spaced repeating patterns (pixels) inline inspections are possible using relatively simple methods. However, the pad area, which transfers digital signals to the TFT active pattern, is composed of patterns with complex shapes in the form of either shape changing patterns or repeating patterns with varying periods. This makes it very difficult to examine complex patterns in the pad area, because repeating patterns have to be divided into areas with similar repeating frequencies and the inspection parameters have to be set differently, and can cause a bottleneck preventing inspection yield improvements. In this paper, an inspection method using a discrete Fourier transform (DFT) filter and a local threshold binarization method is proposed. We show that the proposed method works on an image having both impedance matching and fan-out areas at a varying frequency with a single parameter set. This setting of parameters also can be done in an automated way.

Fast reconstruction of defect profiles from magnetic flux leakage measurements using a RBFNN based error adjustment methodology

Magnetic flux leakage (MFL) inspection is one of the most commonly used electromagnetic in-line inspection methods for detecting anomalies due to corrosion in the underground pipelines. An effective defect reconstruction method is very important for MFL detection. This study proposes a fast radial basis function neural network (RBFNN) based error adjustment (EA) methodology to reconstruct the defect profiles from MFL measurements. In the proposed model, the defect profile is updated according to the difference between the estimated and actual signals. The specific updating scheme is determined by the well trained RBFNN according to the difference. This profile updating strategy ensures that this method can approximate the actual profile faster than other methods. The effectiveness of the proposed algorithm is demonstrated by simulation and experimental data under various conditions. The results demonstrate that the proposed model exhibits faster convergence performance in a robust and stable manner while maintaining good reconstruction accuracy.

Failure Analysis of a Natural Gas Pipeline Rupture

Abstract This article describes the investigation conducted by the National Transportation Safety Board following an explosion triggered by a natural gas pipeline rupture. In addition to determining the likely cause of the rupture, the NTSB issued safety recommendations calling for hydrostatic pressure testing of similar gas lines and making them more amenable to inline inspection methods.

Quantitative measurement of voltage contrast in scanning electron microscope images for in-line resistance inspection of incomplete contact

An in-line inspection method for estimating defect resistances from the grayscale of voltage contrast in scanning electron microscope (SEM) images of manufactured patterns was developed. This method applies a circuit simulator to calculate the intensity of the secondary electrons according to an equivalent-circuit model considering the charge-up voltage on the patterns. To accurately estimate the resistance of defects formed in a device, first, the simulator was improved by considering the variation in defect resistance, which strongly depends on the differential voltage between the plug surfaces and the backside wafer. The defect resistances were obtained from the measured current-voltage (I-V) characteristics of the intentional defect on the standard calibration wafers, in which some incomplete-contact defects were systematically formed. Next, to consider the effect of the electronic characteristics of the pattern under the normal plugs on the grayscale, the I-V characteristics of the normal plugs were measured. The equivalent circuit of the simulator was improved by taking into account the measured I-V characteristics. The calibration curve for the manufactured patterns was calculated from the improved circuit simulator. Finally, the inspection method was applied to estimate the resistance of defects formed on an static random access memory (SRAM) pattern. The calculated calibration curve was used to estimate the defect resistance from the voltage contrast formed on the defects in the manufactured SRAM patterns. The accuracy of the estimation was about an order of magnitude.

Quantitative measurement of voltage contrast in scanning electron microscope images for in-line resistance inspection of wafers

We develope an in-line inspection method for partial-electrical measurement of defect resistance, which is quantitatively estimated from the voltage contrast formed in a scanning electron microscopy (SEM) image of an incomplete-contact defect. We first manufacture standard calibration wafers for the voltage-contrast calibration. The contact resistance of systematically formed defects varied from 108 to 1017 . Then, we quantitatively analyze the gray scales of these defect images captured using a review SEM. As a result, calibration curves for estimating the contact resistance of the incomplete-contact defect are obtained at a probe current of 60 pA and a charging voltage of 4 V. The estimated contact resistance is between 107 and 1012 . Finally, this inspection method is applied to wafers manufactured for a static random access memory device. Accordingly, the gray scales of defective plugs formed for shared contact patterns are classified into two levels. The resistances of these defects are estimated from the calibration curve. The estimated resistances of the lower contrast defects (with an accuracy of about one order of magnitude) agree well with the resistances measured using a nanoprober. The resistances of the higher contrast defects are estimated as well, although they are too high to be measured using a nanoprober.

Magnetic flux leakage (MFL) technology for natural gas pipeline inspection. Topical Report, November 1992 : Bubenik, TA.; Nesdroth, J.B.; Eiber, RJ.; Saffell, B.F. Battelle Memorial Inst., Columbus, Ohio (United States), PB93181899/GAR, 125 pp. (Nov. 1992)

The report is one of a series of four topical reports published by the Gas Research Institute (GRI) on the methods being used by pipeline operators to assure the integrity and safe operation of the domestic natural gas transmission system. The pipeline system in the United States has been constructed over the past half century. Assuring the integrity of the system will provide long-term serviceability to meet the future demands for natural gas. The topical report summarizes the current state of the most commonly used in-line inspection method, magnetic flux leakage (MFL). Not everything about MFL technology is known. Rather, the report presents current information on MFL techniques for pipeline inspection as they are understood by the authors. MFL is an in-line inspection method used to inspect gas-transmission pipelines. MFL inspection tools are completely self-contained units that move through pipelines at 2 to 6 miles per hour. All MFL tools have systems for magnetizing, sensing, data recording, and power. MFL tool performance is affected by many design and operational parameters. On-going research efforts are aimed at more fully understanding these parameters. MFL can generally detect metal-loss corrosion in pipelines. However, MFL cannot reliably detect all types of anomalies.