In-line Inspection Tools Research Articles

In-Line Inspection Methods and Tools for Oil and Gas Pipeline: A Review

In-Line Inspection Methods and Tools for Oil and Gas Pipeline: A Review

Challenges and Approaches to Extending the Safe Operational Life of Future Renewable Energy Infrastructure

To decarbonize our economy, a huge network of renewable energy infrastructure will need to be built both onshore and offshore, often at remote locations, to generate and transmit renewable energy, and to convert and store renewable energy. Corrosion and other forms of materials degradation will pose major challenges to critical components of renewable energy infrastructure that often operate in extreme and complex environmental conditions. Currently solar and wind farms are designed only for approximately 25 years due to the degradation of solar panels and wind turbines. Such a short design life is unsustainable, not only from lifecycle assessment point of view, but also for generating significant materials wastage. The lives of batteries, hydrogen electrolysers and fuel cells are also significantly affected by corrosion and materials degradation. Addressing these issues is critical for the feasibility and sustainability of future renewable energy-based economy. Corrosion engineering is expected to play an important role in the emerging renewable energy economy in order to address these issues and challenges. Unfortunately, although substantial progress has been made in corrosion science and engineering over the past decades, major weaknesses exist in the management and control of corrosion of engineering structures exposed to complex engineering environments. This problem becomes even more acute when complex forms of localized corrosion occur on ‘invisible’ and highly variable engineering structures such as underground energy pipelines and offshore wind farms. In most practical cases, the failure of engineering structures is due to unexpected and complex forms of localized damages. The safe service life of an engineering structure is often determined by the ‘worst case scenario’ localized corrosion such as corrosion under disbonded coatings on buried steel pipelines. Effective control and management of localized forms of corrosion in complex environments is probably the most significant issue that has not yet been sufficiently addressed by corrosion engineering. In particular, the prediction, detection and prevention of localized forms of corrosion in complex environments remain the most significant challenges to corrosion engineering.This paper critically reviews the challenges and issues in extending the safe operational life of renewable energy infrastructure in complex environments and discusses potential approaches to overcoming these challenges. It is shown that localized corrosion control is a critical issue that has to be addressed in corrosion science and engineering. Although various approaches to localized corrosion control can be taken in the future to help addressing these issues and challenges, the most practical approach to addressing major challenges of localized corrosion to future renewable energy infrastructure could probably be based on reliable and more effective localised corrosion prediction, detection and control. A prerequisite for achieving such effective and reliable corrosion management is timely knowledge about the initiation, propagation and seriousness of localized corrosion occurring over an engineering structure in order to take timely corrective actions. Currently accurate and reliable localized corrosion prediction in practical engineering structures is very difficult, if not impossible. Current industrial knowledge of localized corrosion is mostly from time based routine inspections using various condition assessment and in-line inspection tools. Although corrosion data from such inspection are useful for identifying longer-term corrosion trends, they often do not have sufficient temporal and spatial resolutions required for predicting dynamic changes in complex localized corrosion including these in renewable energy systems. Acquiring corrosion data more frequently and more reliably is necessary not only for early warning of unanticipated structural failure but also for evaluating the efficiency of corrosion control measures such as cathodic protection, corrosion inhibitors and protective coatings. It is concluded that future corrosion management of renewable energy infrastructure will need to incorporate advanced corrosion monitoring tools, data analytics, artificial intelligence and predictive modelling in order to achieve quantitative, accurate and reliable corrosion prediction and closed-loop smart corrosion control. More specifically, (i) the maintenance and management of future energy infrastructures will require more reliable and accurate corrosion forecast and prediction capabilities for more effective detection, monitoring and control of corrosion on infrastructure exposed to complex and variable environments. (ii) Future energy infrastructure will require more efficient corrosion protection technologies, especially those able to control localized forms of corrosion, in order to extend the service life of future renewable energy infrastructures. (iii) New eco-informed and environmentally friendly anti-corrosion materials and methods will be required to meet progressively strict regulations for environmental protection. Cases are presented from the author’s recent research and development over the past decade.Key references:Y Tan, Heterogeneous Electrode Processes and Localized Corrosion, Wiley (2012)Y M Tan, Localized Corrosion in Complex Environments, Wiley (2023)

Pipeline Feature Identification Using a Guided Wave-Based In-Line Inspection Tool

Pipeline Feature Identification Using a Guided Wave-Based In-Line Inspection Tool

Estimation of burst pressure of pipelines with interacting corrosion clusters based on machine learning models

Pipeline corrosion defects mostly appear in a colony such that they interact to reduce the failure pressure, which is not defined by features of a single corrosion defect. The huge amount of corrosion defects captured by in-line inspection tools including the variability of defect profile in pipelines and the dependence of the reliability assessment on such data pose significant research challenges in performance assurance. This highlights the need for computationally efficient modelling schemes to estimate the burst pressure of pipelines affected by both longitudinal and circumferential interacting corrosion defects. In the present paper, a novel approach is proposed for this purpose by combining supervised machine learning methods with 25 numerical models of corroded pipelines, validated with experimental results available from literature. Additionally, six improved composite defect shapes are proposed, resulting in 150 models to examine the non-linear behaviour of interacting corrosion defects by capturing the real the defect profiles captured by the In-line Inspection tools. The predicted failure pressures from the developed numerical models produced an absolute mean deviation of not exceeding 2.03% and 2.2% from the experimental burst pressure and the modified Mixed Type Interaction approach respectively, better than published results from the literature. Notably, the predicted failure pressures based on real pipeline data, infused with the generated artificial neural networks and non-linear regression models provide a total mean deviation of 3.1% and 7.3% respectively, thereby providing a path for effective maintenance planning.

Basal Plane Dislocation Slip Band Characterization and Epitaxial Propagation in 4H SiC

Correlation of X-ray topography and production line defect inspection tools has demonstrated the capability of in-line tools to differentiate between geometrically comparable basal plane slip bands (BPSB) and bar stacking faults (BSF) on 4H SiC wafers. BPSBs were found to propagate through epitaxial growth at high rates and with similar photoluminescence signatures to post-epitaxy BSFs. Molten KOH etching post-epitaxy provided evidence of distinguishing features between BPSBs and BSFs, suggesting that the defects were indeed correctly identified by in-line defect inspection tools pre-epitaxy.

Dynamic Stress Analysis of Multisegment Curved Pipes Subjected to the Passage of an In-Line Inspection Tool

AbstractPipelines are susceptible to degradation over time due to different types of defects caused by environmental and loading conditions. In-line inspection (ILI) is a preventive examination method widely used to monitor the degradation of pipelines. The passage of an ILI tool through a segment of a pipeline with loose boundary condition can generate significant dynamic stress within the pipe. When pipelines pass through excavated sites, bridges, water, and bog, or have free-span segments, they are at a greater risk of dynamic stress. This research aims to study the effects of passing an ILI tool through pipelines consisting of straight and curved segments. A three-dimensionial finite element (FE) model based on the Timoshenko beam theory is developed to model the vibration response of curved pipes during the passage of an ILI tool. Lab-scale experiments are performed to verify the simulation results of the developed FE model. The developed model is further verified through the FE analysis performed in abaqus™ implicit. A comparison of the simulation and experimental results shows that the proposed FE model effectively and accurately predicts the dynamic stress and dynamic displacements of multisegment pipes during the passage of an ILI tool.

A Comprehensive Analysis of In-Line Inspection Tools and Technologies for Steel Oil and Gas Pipelines

The transportation of oil and gas through pipelines is an integral aspect of the global energy infrastructure. It is crucial to ensure the safety and integrity of these pipelines, and one way to do so is by utilizing an inspection tool called a smart pig. This paper reviews various smart pigs used in steel oil and gas pipelines and classifies them according to pipeline structure, anomaly-detection capability, working principles, and application areas. The advantages and limitations of each sensor technology that can be used with the smart pig for in-line inspection (ILI) are discussed. In this context, ultrasonic testing (UT), electromagnetic acoustic transducer (EMAT), eddy current (EC), magnetic flux leakage (MFL), and mechanical contact (MC) sensors are investigated. This paper also provides a comprehensive analysis of the development chronology of these sensors in the literature. Additionally, combinations of relevant sensor technologies are compared for their accuracy in sizing anomaly depth, length, and width. In addition to their importance in maintaining the safety and reliability of pipelines, the use of ILI can also have environmental benefits. This study aims to further our understanding of the relationship between ILI and the environment.

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.

Training Deep Neural Networks with Novel Metaheuristic Algorithms for Fatigue Crack Growth Prediction in Aluminum Aircraft Alloys.

Fatigue cracks are a major defect in metal alloys, and specifically, their study poses defect evaluation challenges in aluminum aircraft alloys. Existing inline inspection tools exhibit measurement uncertainties. The physical-based methods for crack growth prediction utilize stress analysis models and the crack growth model governed by Paris’ law. These models, when utilized for long-term crack growth prediction, yield sub-optimum solutions and pose several technical limitations to the prediction problems. The metaheuristic optimization algorithms in this study have been conducted in accordance with neural networks to accurately forecast the crack growth rates in aluminum alloys. Through experimental data, the performance of the hybrid metaheuristic optimization–neural networks has been tested. A dynamic Levy flight function has been incorporated with a chimp optimization algorithm to accurately train the deep neural network. The performance of the proposed predictive model has been tested using 7055 T7511 and 6013 T651 alloys against four competing techniques. Results show the proposed predictive model achieves lower correlation error, least relative error, mean absolute error, and root mean square error values while shortening the run time by 11.28%. It is evident through experimental study and statistical analysis that the crack length and growth rates are predicted with high fidelity and very high resolution.

Data Analysis System Based on REST Architecture for In-Pipe Inspection

To solve the problems of high maintenance cost, low reusability and poor scalability of in-pipe inspection data analysis system, an in-pipe inspection data analysis system based on REST (Representational State Transfer) architecture is designed and implemented. A multilayer service-oriented architecture based on REST is designed, which decouples the functions of client, middleware, server and data storage to improve the maintainability of the software. REST APIs (Application Program Interfaces) based on HTTP (Hypertext Transfer Protocol) are designed, which encapsulate the core functions such as data analysis, signal processing, automatic identification and quantization into language and platform independent services to meet the needs of multiuser, cross platform and online data analysis. An adaptation method of in-line inspection tool based on metadata is designed, which abstracts the in-line inspection tool into a separate metadata file and decouples it from the client and server programs to improve the scalability of the software. Practice has proved the architecture can improve the maintainability, reusability and scalability of the software, and provide a basis for constructing online in-pipe inspection data analysis service based cloud.

Design and Numerical Simulation of an Odometer Wheel Used in an Ultrasonic In-Line Inspection Tool

An odometer wheel is used to measure forward distance for a piezoelectric ultrasonic device, and it plays a key role in locating defections. Buckling and skidding are its main problems in applications. Based on a self-developed in-line inspection tool, an odometer wheel was designed according to mechanical design principles. A finite element model on scale of 1 : 1 was built to simulate mechanical performance of the odometer wheel. Results show that the maximum Mises stress under the worst condition is far less than the elastic limit of chosen stainless steel, and deformations of swing arms are slight. Acting forces under different conditions also meet requirements of antiskid. It indicates that the designed object has good performance in terms of structure stability and skid prevention.

An efficient adaptive combined filtering method for pipeline bending strain based on inertial in-line inspection

Long-distance high-pressure oil and gas pipelines are threated by various external forces, such as earthquakes, landslides, and other third-party damages. These additional stresses will cause the pipeline to move and bend, which not only causes the pipeline to undergo a large bending strain, but will also causes the pipeline to fail in severe cases. In-line inspection (ILI) based on strapdown inertial navigation technology has become an effective inspection method for long-distance pipelines in recent years. However, the cups or support wheels of the ILI tool encounter the pipe wall, girth or spiral welds, and other features that lead to minor oscillations and vibration during the inspection. The attitude information computed by the inertial measurement unit (IMU) is affected by these noises, and the computation of pipeline bending strain (PBS) is inaccurate. In this paper, an efficient adaptive combined filtering method based on wavelet transform with Savitzky–Golay (S-G) is proposed, which is adaptable to the variation of attitude information for the ILI tool during a long distance and time inspection. Experiments were conducted to demonstrate the effectiveness of the optimization for PBS. The average relative deviation decreased from 38.3% to 18.3%.

Application of non-contact quantum efficiency measurement for solar cell fabrication process insights

We demonstrate the application of non-contact external quantum efficiency (EQE) measurements as potential in-line inspection for solar cell mass production. The technique, based on electroluminescence excitation spectroscopy (ELE), is several times faster than conventional contacted EQE measurements, and enables on-the-flight contactless assessment of spectral response. To simulate a manufacturing environment, EQE of a batch of 200 bifacial PERC solar cells are investigated. This allows statistical analysis of the interplay between fabrication process conditions, cell current–voltage characteristics and results from other in-line inspection tools. The study shows that non-contact EQE can be used to detect variation in POCl3 diffusion and PECVD processes at cell level and without direct contact to the device. It is also possible to predict EQE on passivated, non-metallized diffused wafers. This provides powerful information in predicting end-of-line cell parameters, as well as to sort and remove low performing wafers prior to printing to eliminate metal paste wastage.

Operational subsea pipeline assessment affected by multiple defects of microbiologically influenced corrosion

This paper presents a systematic approach to evaluate the time interval of optimal maintenance strategy for the subsea process system influenced by Microbiological Influenced Corrosion (MIC) within multiple defects. The proposed method incorporates the non-homogeneous Poisson, homogeneous gamma, and non-homogeneous Markov processes for modeling the generation of multiple defects, the average pit depth growth, and maximum pit depth, respectively. The maintenance strategy comprises industrial procedure, probability of failure detection, errors sizing in-line inspection tools, management actions costs, and failure cost. The developed framework simulates maintenance strategies considering time interval, cost, probability of detection, average pit depth, and maximum pit depth and identifies the optimal strategy. The practical application is demonstrated in a North Sea subsea pipeline system under MIC’s influence. This work assists decision-makers in selecting the optimal conditioned-based maintenance strategy for the processing system. While the application is demonstrated to subsea process systems under MIC influence, the developed approach is equally applicable to other process systems.

Dynamic Stress Analysis of Exposed Pipes Subjected to a Moving In-Line Inspection Tool

AbstractIn-line inspection (ILI) is a non-destructive assessment method commonly used for defect assessment and for pipeline monitoring. Passing an ILI tool through an excavated or exposed section of a pipe during an integrity assessment can excite vibrations. The ILI tool’s weight and speed can exert substantial forces, stresses, and deflections on the pipe section. When the excitation frequency from the ILI tool’s movement is close to the pipe’s natural frequency, the dynamic stress generated within the pipe can become great enough that it creates integrity concerns on the pipeline. This research aims to study the effects of an ILI tool’s passage through exposed and partially supported pipes under a variety of boundary and loading conditions. A finite element model of an exposed pipe section is developed based on the Timoshenko beam theory to predict the pipe’s displacement, strain, stress, and frequency responses under a wide range of excitation frequencies. The model is further validated using a lab-scale experimental setup with a mass that moves at different speeds. A comparison between the simulation and the experimental results shows that the proposed model can effectively predict the pipe’s dynamics.

Coherent Fourier scatterometry using orbital angular momentum beams for defect detection.

Defect inspection on lithographic substrates, masks, reticles, and wafers is an important quality assurance process in semiconductor manufacturing. Coherent Fourier scatterometry (CFS) using laser beams with a Gaussian spatial profile is the standard workhorse routinely used as an in-line inspection tool to achieve high throughput. As the semiconductor industry advances toward shrinking critical dimensions in high volume manufacturing using extreme ultraviolet lithography, new techniques that enable high-sensitivity, high-throughput, and in-line inspection are critically needed. Here we introduce a set of novel defect inspection techniques based on bright-field CFS using coherent beams that carry orbital angular momentum (OAM). One of these techniques, the differential OAM CFS, is particularly unique because it does not rely on referencing to a pre-established database in the case of regularly patterned structures with reflection symmetry. The differential OAM CFS exploits OAM beams with opposite wavefront or phase helicity to provide contrast in the presence of detects. We numerically investigated the performance of these techniques on both amplitude and phase defects and demonstrated their superior advantages-up to an order of magnitude higher in signal-to-noise ratio-over the conventional Gaussian beam CFS. These new techniques will enable increased sensitivity and robustness for in-line nanoscale defect inspection and the concept could also benefit x-ray scattering and scatterometry in general.

In-Depth Study of 3D Color-Resist Coating Process for Optically Uniform Image Sensors

The color filter required for manufacturing a CMOS image sensor was redeveloped to optimize its optical uniformity. An in-depth study of the three-dimensional (3D) coating process and how it gives rise to various radial-shaped striation patterns was conducted. These radial-shaped striation patterns were systematically investigated with reference to two types of patterns: the orthogonal type found only at the orthogonal edges of the wafer and the diagonal type found mostly at the corner of each quadrant. The formation of the orthogonal pattern was based on the wide standing wave created by the incident force of the spreading color photoresist (PR) and the reflective force from the bump pads acting as coating barriers. The diagonal pattern was found to be generated by the turbulent wakes created behind the bump pads by the drag force, which interfered with the coating flow. An in-depth study using Ansys CFX software and an in-line inspection tool revealed that lowering the viscosity of the color PR material is a key factor for improving the phenomenon whereby the 3D striation patterns of the orthogonal and diagonal types are formed. Based on this finding, it was possible to drastically reduce the formation of the 3D striation patterns by decreasing the viscosity of the material comprising each color PR. This study provides not only an empirical and theoretical understanding of the 3D color PR coating mechanism, but also guidelines for future color filter processes.

Multidimensional data statistical processing of magnetic flow leakage signals from a Colombian gas pipeline

The hydrocarbon industry in Colombia is one of the principal pillars for the Colombian economy, representing around 5% of its gross domestic product. Since petroleum reserves have decreased, gas becomes one main alternative for economical growth. However, current gas pipelines have been in service for over 30 years and some of them are buried and phenomena, such as metal losses, corrosion, mechanical stress, strikes by excavation machinery, and another type of damages, are presented. The maintenance program of these structures is typically corrective type and is very expensive. To overcome this situation, the native research institute “Research Institute of Corrosion—Corporación para la Investigación de la Corrosión” recently developed an in-line inspection tool to be operated in Colombian gas pipelines to get valuable information of their current state along thousands of kilometers. A huge quantity of data is recorded (including tool movement, magnet, magnetic flow leakage, and caliper signals), which demand a high-computational cost and an adequate tool analysis to establish the current pipeline structural health condition. In this sense, authors have shown in several works that principal component analysis is an effective tool to detect and locate abnormal operational structural conditions from multidimensional data. In a previous analysis, multidimensional data were used to locate possible damages along the pipeline. However, most of the activated points belonged to weld points. Then, in this article, it is proposed to use the root mean square value of magnetic flux leakage signals to separate these points and to obtain sets of signals by sections removing the welds, and then multiway principal component analysis is applied for each set of signals of each gas pipeline section. The maximum values of damage indices (Q and -statistics) of each section are conserved to activate the sections of the gas pipeline with more probability of damages and then, they must be evaluated by experts.

SMART manufacturing through predictive FA

We use semiconductor Integrated Circuit (IC) chips in our day-to-day life. ICs are placed in electronic devices that have become integral part of human life. With the advancement in human's modus vivendi, there comes a requirement of fast-growing technology which can only be achieved by narrowing the gap between design and manufacturing communities. As the semiconductor industry has matured over the year with reduced technology nodes, with 3D (3-Dimensional) structures, there has been a constant emphasis on yield, quality and reliability of the ICs. The technology and process complexity of today's ICs demand testing to monitor yield, performance, and reliability be performed during the manufacturing process. Chipmakers are using various tools to avoid losses with respect to quality and cost of the chip. With great challenge to find defects on lower technology node, comes the need for more accurate and efficient way of inspection. Failure Analysis (FA) results in the conventional process are no longer relevant to designs on new technology nodes. The proposed method in the paper, the FA results are fed back to upstream manufacturing process for corrective action. In this environment, traditional FA activities are finding an expanded role: Predictive FA. Proposed paper is an effort to overcome the traditional FA scenario using Predictive FA. A subset of artificial intelligence (AI), machine learning (ML) comes in rescue for the same. Integrating the algorithms of ML with image processing and pattern search to find the unique patterns of defects by making the machine to build a library (training data) of all the defects observed during FA and use the learning on test data. It will generate ability to predict new defects after being trained with enough scenarios. One such learning type is reinforcement learning, the machine is exposed to an environment where it trains itself continually. It learns from experience and try to make accurate decisions. This learning loop from FA to the inline inspection tools aids to better analysis of defects at a prior stage and detect potential failures in chip.

Failure pressure prediction by defect assessment and finite element modelling on natural gas pipelines under cyclic loading

In this work, a 3-dimensional finite element (FE) model was developed to investigate the effect of cyclic loading, which is induced by vibration during operation of in-line inspection (ILI) tools, on local stress and strain distributions and failure pressure of an X80 steel natural gas pipeline containing a corrosion defect. Modelling was also conducted on a low-grade X60 steel pipe for comparison. Parametric effects, including internal pressure, R-ratio, cyclic frequency and dimension of the corrosion defect (primarily the defect depth), were determined. The cyclic loading greatly increases the von Mises stress and strain at the corrosion defect and reduces the threshold internal pressure to cause plastic deformation at the defect. As the internal pressure increases, both the von Mises stress and the strain increase and the high stress/strain zones expand along the defect length direction. The local stress and strain at the corrosion defect increase with decreased R-ratio and cyclic frequency, resulting in a reduction of failure pressure of the pipeline. An increased defect depth enhances local stress and strain concentrations, reducing failure pressure of the pipeline. A novel method is developed to assess corrosion defect during ILI tool operation and predict the failure pressure of pipelines under cyclic loading for the first time of its kind.