In-line Inspection Research Articles

A risk-based approach to inspection planning for pipelines considering the coupling effect of corrosion and dents

RBI, referring to a risk-based approach to inspection planning, is an established pipeline integrity management method. Both corrosion and dents are the primary threats to pipeline integrity. However, they are often treated separately in RBI without considering their interactions. This coupling may lead to a synergic effect on integrity degradation. The present study proposes an RBI planning framework for pipelines considering external corrosion and dents. Time-dependent pipeline deterioration by dents and corrosion is modeled probabilistically using a Dynamic Bayesian Network (DBN), in-line inspection (ILI) data, and corrosion propagation knowledge. Two failure scenarios (leakage and burst) are considered. The hybrid method, integrating Monte Carlo Simulation (MCS) and Latin Hypercube Sampling (LHS) technique, estimates the pipeline's Probability of Failure (PoF) over time. The pipeline failure risk is quantified by monetizing the Consequence of Failure (CoF). An optimization model of loss-maintenance total expected cost is introduced to determine the optimum inspection period using maximum acceptable risk (MAR) and the lowest total expected cost. A cost-benefit analysis (CBA) is finally implemented to choose appropriate risk reduction measures. The proposed framework is robust and well-validated by a case study on an in-service pipeline.

Importance sampling-based probabilistic performance modeling of low-, mid- and high-strength pipelines under coupling effect of hydrogen-induced damage and corrosion

In-service long-distance pipelines are a practical approach to implementing large-scale hydrogen transportation under the context of energy transition. However, the hydrogen-induced damage (HID) and corrosion would deteriorate the material properties and load-bearing capacity of the pipelines. In the present study, the coupling effect of HID and corrosion are probabilistically incorporated into the mechanical characterization models for low-, mid- and high-strength pipelines. Spatiotemporal stochastic growth is modeled using the stochastic gamma processes with significantly affected defect geometric parameters to facilitate the uncertain performance status of pipelines. All unknown parameters of the proposed method can be inferred by inducing in-line inspection (ILI) data. The computational framework for importance sampling (IS) has been developed to estimate the system failure probability of corroded natural gas pipelines considering HID. Apply several programs to conduct probability analysis to demonstrate the applicability and effectiveness of the proposed method. The results demonstrate that HID affects the failure behavior and safety probability state of pipelines containing corrosion defects. The performances of various strength pipelines have significant differences in deterioration paths due to HID, causing additional maintenance or potentially disastrous consequences. The proposed method has been proven to capture the potential uncertainties of HID and corrosion on different-strength steel pipelines, which is beneficial for the lifecycle management of blended hydrogen natural gas pipelines.

An ultrasonic in-line inspection data processing method considering invalid data caused by sensor failure

Ultrasonic in-line inspection is one of the most widely adopted nondestructive testing methods for defect detection in pipelines. In practical industrial scenes, pipeline specification varies, sensor failure occurs frequently, and the number of pipeline defect samples is scarce. How to detect defects without false detection caused by invalid data and with limited labeled samples is a challenging problem in this area. An ultrasonic in-line inspection data processing method considering invalid data caused by sensor failure is proposed in this paper to enhance the accuracy of the defect and its profile detection. Firstly, the multi-channel data is aggregated according to sampling time. The data dimension is reduced, and the accuracy of the invalid waveform detection adopting isolation forest arithmetic is improved. The methods are adopted for the invalid waveforms replacement with two-dimensional cubic spline interpolation using adjacent sensors. Secondly, a natural breakpoint method is adopted to locate the echo peaks. Residual wall thickness is evaluated by calculating the time difference between the echo peaks. A fast pseudo-colorization method based on sliding windows and a morphological image processing method are proposed to detect defects and their profiles efficiently utilizing the residual wall thickness. Finally, practical in-line inspection data is utilized to evaluate the performance. The experiment results illustrate that the detection accuracy is enhanced on different sizes of pipelines without requiring labeled samples.

An intelligent feature recognition method of natural gas pipelines based on shapelet and blending fusion model

Despite the availability of pipeline bending strain detection technologies based on inertial measurement unit, there is a lack of intelligent and efficient methods for accurately identifying pipeline features by bending strain. Therefore, this paper proposes a novel method for identifying features in natural gas pipelines based on shapelet and blending fusion model. Specifically, the shape features of the bending strain data are extracted and transformed by shapelet. Then a blending fusion model with SVM, Decision Tree and Gradient Boosting as base learners and Random Forest as meta-learner is constructed. Finally, the extracted features are fed into the blending fusion model for pipeline feature recognition. The model is trained with bending strain data obtained from a real natural gas pipeline, the results indicate that the recognition accuracy of the proposed method is 97.17%. Compared with other models, the superiority of the proposed model is verified, and it is proved that the proposed method has better accuracy than the existing models (over 1.3%). Overall, the method proposed in this paper can be effectively combined with the in-line inspection system to provide a reference for pipeline companies to carry out pipeline integrity management.

Simulating non-homogeneous non-Gaussian corrosion fields on pipelines based on inline inspection data

This study presents a methodology to simulate non-homogeneous non-Gaussian corrosion fields on the external surface of buried steel pipelines by using inline inspection (ILI) data. It is assumed that the non-homogeneous non-Gaussian corrosion field consists of multiple homogeneous non-Gaussian anomalies that can be characterized by the marginal distribution and spatial autocorrelation function of the corresponding corrosion depths. Based on corrosion data collected from in-service pipelines, empirical relationships are developed to estimate parameters of the marginal distribution and autocorrelation function from the ILI information. To generate a synthetic corrosion field, one first generates realizations of corrosion anomalies and then merge the generated anomalies into a single non-homogeneous field by applying a spatial modulating function to each anomaly. The proposed methodology will improve the accuracy of the fitness-for-service assessment of corroded pipelines in practice as the burst capacity of the corroded pipeline can be more accurately evaluated by using the synthetic corrosion field than using the idealized corrosion field obtained from the ILI data.

Noncontact laser ultrasonic inspection of weld defect in lithium-ion battery cap

The continuous development of electric vehicles and electronic devices has increased the demand for lithium-ion batteries. In this study, a laser ultrasonic inspection system was developed for the noncontact and nondestructive inspection of the laser welding conditions of a cylindrical lithium-ion battery cap. An Nd: YAG pulse laser was used for Lamb wave generation on the battery cap. Lamb waves were measured from the front surface of the battery cap using a laser Doppler vibrometer. The bending stiffness of the battery cap decreased as the welding condition deteriorated, and the Lamb wave amplitude was amplified. Accordingly, an automated defect detection algorithm for inspecting welding conditions was developed. The effectiveness of the proposed inspection system was theoretically and experimentally evaluated using actual cylindrical battery caps. The battery cap weld inspection accuracy exceeded 89 %, correctly identifying seven defective and ten intact battery caps out of eight defective and 11 intact battery caps, respectively. The numerical simulation indicated that the Lamb wave amplitude increased at the weld joint, demonstrating consistency with the experiment results. The proposed system is promising for the in-line inspection of battery caps because it facilitates high-speed, noncontact, and nondestructive inspection. Furthermore, the proposed inspection system can detect invisible weld defects on the back surface of a battery cap by inspecting its front surface.

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.

Data-driven model to predict burst pressure in the presence of interacting corrosion pits

This paper presents a data-driven approach to predict the pipelines’ corrosion-induced Burst failure. In this approach, different aspects of pit growth progression and spatial distribution of pits are simulated. The proposed approach takes advantage of population characteristics to model these aspects of the degradation paths for each pipe section down to the size of single joints. The insights obtained from simulations are used to project the degradation of each pipe section. Understanding corrosion behavior and field data are used to model the corrosion-related parameters such as corrosion pit dimensions, probability and time of initiation, and location. The failure is modeled using the probabilistic simulation considering degradation rate, interactions among pits, and material properties as stochastic variables. The proposed approach and included models are tested using multiple real-life inline inspection datasets. Validation of predicted properties shows prediction errors ranging from 3%–10% depending on the three remaining strength calculation approaches. This work aimed to serve as an important tool for risk-based maintenance prioritization, inspection interval assessment, and the fitness of service assessment of pipelines.

Methodology for ranking internal corrosion-induced leakage susceptibility in oilfield pipelines

ABSTRACT This investigation focuses on the ranking of pipeline leakage susceptibility of two categories of pipeline systems. One consists of three oilfield water-handling pipeline systems and the other consists of five dry crude systems. Online corrosion monitoring (OCM) method was used for both systems, forthermore, in-line inspection (ILI) method, with Magnetic Flux leakage (MFL) tool, was also used for the dry crude systems. The OCM leakage susceptibility potential is derived from the combined severity of general corrosion, pitting corrosion, fluid corrosivity and sessile bacteria population density in the process streams. The ILI leakage susceptibility potential is derived from the average number of corrosion anomalies greater than 10% of the wall thickness and the maximum depth of the anomalies (as percentage of the wall thickness). In spite of such distinctions, the ranking of pipeline leakage susceptibility for the five dry crude pipeline segments turned out to be identical for the two methodologies.

Unveiling dopant concentration in boron doped Si ultrathin film: Enhanced analysis using time-dependent second harmonic generation

The evaluation of dopant concentration is essential in fabricating advanced field-effect transistors. In this study, we effectively leverage the unique characteristics of boron traps in the in-situ boron (B) doped Si ultrathin film (DSUTF) to examine the evolution of electric field through time-dependent second harmonic generation (TD-SHG). During the laser irradiation, internal photoemission (IPE) occurs in our system due to multiphoton absorption which enable the electron injected from Si to SiO2. Our findings demonstrate that the presence of oxygen molecules weakens the electric field, resulting in a consequential reduction in SHG intensity. Therefore, we propose a model to elucidate the relationship between electrons and oxygen. Furthermore, we observe a robust monotonic correlation between the boron induced electric field and the dopant concentration. These results are consistent with first-principles calculation and capacitance-voltage measurement. Utilizing TD-SHG method for inspection of B dopant concentration in DSUTF provide a productive way for in-line inspection in semiconductor fabrication.

High-speed, multi-zone ultrasonic inspection of bar and wire stocks with an in-line phased array inspection system

Typical standards in aerospace applications require the ultrasonic testing (UT) of bar and wire stock with a highly reliable detection of small defects over a wide depth range in the material. Characteristic reference reflectors are flat bottom holes (FBH) with diameters smaller than 1 mm. Typically, such inspections are done with immersion tanks using a set of probes optimized for each depth. However, for industrial applications there are disadvantages such as slow scan speed, the time-consuming inlet and outlet process, complicated mechanics and significant costs of the specialized probes. This contribution demonstrates an in-line phased array inspection system with integrated multi-zone inspection, which is based on the Krautkrämer ROWA™ Ux machine. After a short description of the setup special focus is given on the implemented testing concept which is optimized with respect to a multi-zone inspection and includes a dedicated automated adjustment of the system. Furthermore, the powerful electronics allows a flexible setting of focus point, aperture size and filtering. This significantly reduces the need for special probes and by means of digital depth focusing (DDF) also the number of test shots. At the end, the flaw detectability in different depths is illustrated and achievable test speeds are presented. Moreover, external boundary conditions are discussed.

Quality Control of Composite parts by robot guided Terahertz imaging

In this work a new approach for in-line quality inspection of drilling holes in glass reinforced fibre composites (GFRP) is presented. The method relies on Terahertz radiation and its large penetration depth in non-conductive materials. By applying THz reflection imaging, 3D information such as interfaces or defects within the sample can be obtained. As a starting point, a laboratory setup based on an industrial THz time-domain spectrometer (TDS) in combination with linear stages for the sample manipulation is built. A software for measurement automatization and data acquisition is developed. Based on measurements performed on drilled GFRP plates, an algorithm highlighting possible defects within the measurement data is developed. The results correlate very well with x-ray tomography (XCT) measurements. Finally, the THz-TDS system is transferred onto an industrial robot and the THz signal synchronized with the position of the robot’s head, expanding the flexibility of the method regarding the applicability to a wide variety of specimen geometries.

Latest Developments in the Hardspot Inspection of heavy plates

Hard Spots are local areas with increased hardness on the surface of semi-finished or end products in steel manufacturing. The cause of these hard spots is attributed to effects in the casting or rolling process. In general, the occurrence of hard spots cannot be avoided completely in production of heavy plates. Therefore, in various current line-pipe specifications the topic ‘hard spots’ is discussed and there is actually a strong request for a suitable test method from end users as well as manufacturers of line pipes. The current presentation should give an update on the ongoing developments. Basis of all current approaches are electromagnetic inspection methods, based on the correspondence in between electrical and magnetic material properties and physical hardness. To be able to integrate an inspection into the production process, the so called rolling skin or mill scale, a thin surface layer of different iron oxides with different material properties to the base material, should not influence the inspection results. The inhomogeneities of this rolling skin, especially in conductivity and permeability, normally render standard eddy current inspection impossible. Additionally in the standard production process, the products were handled by large cranes, manipulating the plates by strong magnets. These magnets leave behind a significant residual magnetization. To overcome both problems the method uses a strong alternating magnetization to measure different resulting micro magnetic parameters. By combining these parameters, the mill scale as well as the residual magnetization effects can be minimized, so it gets possible to measure small local hardness variations on the plates. The presented paper describes the actual progress of the inspection method itself including the theoretical background as well as the calibration on artificial hard spots. Furthermore, an outlook will be given to the implementation in an automatic inline inspection system for plate mills.

Unsupervised deep learning for defect detection on CT parts using simulated data

Computed tomography (CT) is a prominent technology for nondestructive quality control and is already used in industry for defect detection. However, as quality control is shifting towards a full in-line inspection, automatic CT analysis is required to meet the tight production time. Nonetheless, in settings where a high amount of data is produced, a robust fully automatic defect detection is essential In the past years, deep learning (DL) has been extensively used to perform vision tasks in an automatic way, and given its promising results, has been successfully applied in CT settings. Most of the recent work is based on supervised DL often adapted from results in the medical field. Supervised DL, although extremely powerful, has the drawbacks of requiring a high amount of labeled data done by experts and is biased to the specific dataset used. Therefore, an unsupervised DL model is presented. A two stages network formed by an auto-encoder and an autoregressive model, originally implemented for image generation, is adapted for volume segmentation. The network is trained on the specific task of defect segmentation of cast aluminum parts. CAD models of such parts are gathered, and corresponding simulated CT scans are acquired. Results show that the architecture, although originally implemented for data generation, can be adapted for CT volume segmentation.

Nanometer resolution of 2D single-shot low-coherence & comb-based profilometry based on the effect of discrete height of spatial phase modulator surface

A 2D single-shot comb-based interferometer is well known as a powerful tool for inline inspection owing to its high speed, extensive measurement range, and anti-vibration. However, nanometer resolution is still a milestone that has not been achieved in comb-based interferometers owing to the precision limitation caused by the discrete profile of the spatial phase modulator (SPM) device, typically in the range of several micrometers. Therefore, this study confirms 2D single-shot comb-based profilometry at sub-micron and nanometer-scale resolutions. The system first investigates the effect of the discrete limitation of the SPM surface profile and then verifies the submicron detection ability of the optical frequency comb-based setup. It was quantitatively shown that the highest resolution can be obtained by properly setting the step size of the spatial phase modulator and the SPM surface resolution of the imaging optics. The highest profilometry resolution was recorded at 26.4 nm in a short measurement range, while the setup using the optical comb source with millimeters measurement range for industrial application also can achieve a resolution of approximately 259.2 nm. The results demonstrate the system feasibility in overcoming the discrete limitations of the SPM profile. It also demonstrates for the first time that the so-called 2D single-shot comb-based interferometer can achieve nanometer-scale profilometry resolution covering the measurement range from tens of millimeters to sub-micrometer detection.

Hyperspectral confocal microscopy in the short-wave infrared range.

We demonstrate hyperspectral confocal microscopy in the short-wave infrared (SWIR) range of 1100-1600 nm using a wavelength-scanning laser in tandem with laser scanning confocal microscopy. Confocal microscopy in the SWIR range allows for high-resolution inspection of an integrated circuit (IC) chip, while hyperspectral imaging, together with a chemometric analysis, enables us to identify functional circuit block groups in the acquired image. With the extended capability, the developed instrument can be potentially used for inline inspection and non-invasive failure analysis of IC chips.

A Corrosion Maintenance Model Using Continuous State Partially Observable Markov Decision Process for Oil and Gas Pipelines

This paper proposes a continuous state partially observable Markov decision process (POMDP) model for the corrosion maintenance of oil and gas pipelines. The maintenance operations include complex and extensive activities to detect the corrosion type, determine its severity, predict the deterioration rate, and plan future inspection (monitoring) schemes and maintenance policy. A POMDP model is formulated as a decision-making support tool to effectively handle partially observed corrosion defect levels. It formulates states as the pipeline’s degradation level using a probability distribution. Inline inspection (ILI) methods estimate the latest state of the pipeline, which also defines the initial state of the optimization process. The set of actions comprises corrosion mitigation operations. The errors associated with the ILI method are used to construct the observation function for the model. The sum of inspection, maintenance operations, and failure costs for a given state and action are formulated as rewards. Numerical experiments are made based on data collected from the literature. The results showed that different policies, whether derived from solvers (theoretical) or determined from practical experience, can be compared to identify the best maintenance alternative using the model. It was also observed that the choice of the solvers is important since they affect the discounted rewards and the run time to obtain them. The model approximates the parameters and uncertainty associated with the propagation of corrosion, proficiency of inspection methods, and implementation of maintenance policies. Overall, it can be applied to improve the maintenance decision-making process for the oil and gas pipeline as it incorporates the stochastic features of the operation.

Forecasting pipeline safety and remaining life with machine learning methods and SHAP interaction values

In-line inspection (ILI) is a common and useful way to provide comprehensive threat assessments and to detect defect dimensions. However, the ILI is very expensive and most of its costs are unnecessary since very few pipelines contain defects. Many previous studies have used machine learning (ML) methods to predict the safety of pipelines based on the features extracted from ILI signal results, which is very laborious and difficult to apply in industry. In this paper, the ILI results were predicted using multiple ML models through three prediction cases with only pipeline attributes and environmental features as input variables. In the first case, six ML methods were compared to predict whether there are defects in a pipeline section. The Catboost (CAT) method showed the best performance in all evaluation metrics with the highest certain prediction ratio (94%). The second case was using three ML methods to predict a defect depth, defect length, and defect width. The prediction accuracy of the defect depth was much higher than that of the defect length and defect width. A CAT model was the optimal model in this case due to its best performance in all the predictions. In the third prediction case, three ML methods were used to predict a defect length growth rate and defect depth growth rate based on different ILI results from different years. CAT was also the best method in this case with the highest accuracy. The predicted defect length growth rate and defect depth growth rate were used to calculate the remaining lifetime of pipelines based on the maximum allowable defect depth and failure pressure. The remaining lifetime of pipelines with different thickness, land use, and soil types was analyzed with changes in the specified minimum yield strength of a pipeline, a pipeline year, and maximum operating pressure. The accurate predictions in the three cases and the correlation analysis between input features and outputs helped company save a lot of costs and provided valuable information and suggestions in the further progress.

Blending hydrogen in existing natural gas pipelines: Integrity consequences from a fitness for service perspective

Blending hydrogen in existing natural gas pipelines compromises steel integrity because it increases fatigue crack growth, promotes subcritical cracking and decreases fracture toughness. In this regard, several laboratories reported that the fracture toughness measured in a hydrogen containing gaseous atmosphere, KIH, can be 50% or less than KIC, the fracture toughness measured in air. From a pipeline integrity perspective, fracture mechanics predicts that injecting hydrogen in a natural gas pipeline decreases the failure pressure and the size of the critical flaw at a given pressure level. For a pipeline with a given flaw size, as shown in this work, the effect of Hydrogen Embrittlement (HE) in the predicted failure pressure is largest when a failure occurs by a brittle fracture. The HE effect on failure pressure diminishes with a decreasing crack size or increasing fracture toughness. The safety margin after a successful hydrostatic test is reduced and therefore the time between hydrotests should be decreased. In this work, all those effects were quantified using a crack assessment methodology (level 2, API 579-ASME FFS) considering literature values for KIH and KIC reported for an API 5L X52 pipeline steel. To characterize different scenarios, various crack sizes were assumed, including a small crack with a size close to the detection limit of current in-line inspection techniques and a larger crack that represents the largest crack size that could survive a hydrotest to 100% of the steel Specified Minimum Yield Strength (SMYS). The implications of a smaller failure pressure and smaller critical crack size on pipeline integrity are discussed in this paper.

Analysis on error distribution of In-Line inspection in oil and gas pipelines

Analysis on error distribution of In-Line inspection in oil and gas pipelines