Pipeline Condition Assessment Research Articles

Field study on proactive pipe condition assessment using hydroacoustic noise

Condition assessment of water transmission pipelines is of important necessity for prioritizing rehabilitation and preventing catastrophic pipe failure. Hydraulic transient analysis has been investigated for three decades for this purpose and applied to many field studies. However, the significant pipe vibrations caused by the transient generation process and the limited energy and bandwidth of conventional discrete transient pressure waves (e.g., step and pulse waves) limit the accuracy and resolution of the analysis. This paper reports a pilot field study of noninvasive and nondestructive pipe condition assessment using small amplitude and persistent hydroacoustic noise instead of conventional large and discrete hydraulic transient waves. By opening a discharge valve installed at an existing access point, such as an air valve, on the field pipe, hydroacoustic noise was generated at the valve due to the turbulence of the discharge. The hydroacoustic noise was measured by pressure transducers at this access point as well as some other access points along the field pipe. A signal deconvolution process was then applied to the measured hydraulic pressures to transfer them to a compositional impulse response function (IRF), which is composed of IRFs of different pipe sections. A mathematical model was derived to interpret the anomaly-induced spikes on the deconvolution trace. A time-shifting process on the compositional IRFs was proposed to find the locations with pipe condition changes from a specific direction of the pipe. The field study has shown that small-amplitude persistent hydroacoustic noise can replace conventional large hydraulic transient waves for pipe condition assessment.

Machine learning-based outlier detection for pipeline in-line inspection data

Pipeline companies are facing challenges in maintaining the integrity and reliability of their pipelines. They are working towards predictive maintenance using machine learning-based approaches to predicting anomalies. Training machine learning models requires sufficient data. Data quality is therefore becoming important because inaccurate data will lead to an inaccurate or wrong decision on pipeline condition assessment and the following management. This research paper intends to address the data quality issues of pipeline inspection data such as in-line inspection (ILI) data using machine learning models. Different machine learning models developed by random forest regression, linear regression, and nearest neighbors’ methods were tested to detect outliers in the ILI data. In this paper, the ILI data collected from an oil pipeline over a period of 22 years was applied to testing and analysis. To verify the outlier detection results of machine learning models, we used statistical analysis including Z-score method to check and find if there are any gaps in the analysis. It verifies that all these methods show almost the same or very similar results for the detection of the outliers. Hence, this study presents a robust method for the field applications in the pipeline industry.

Develop an Action Plan for Pipeline Condition Assessment

Answering the right questions on pipeline condition assessment in light of system‐specific priorities helps utilities make cost‐effective decisions about infrastructure renewal.

3D laser point cloud-based geometric digital twin for condition assessment of large diameter pipelines

The increasing aging of underground pipe networks and the lack of effective inspection technologies present considerable challenges for whole life-cycle management of these infrastructures. Modern laser scanning technology offers a cost-effective and safe means to obtain dense and accurate 3D topographic data of the inner surface of pipelines. However, laser scanning point clouds contain substantial noise and outliers, and efficiently extracting valuable information for structural and functional mapping remains in its infancy. This paper presents an innovative method for fast processing point clouds data of large-diameter pipelines, enabling the accurate extraction of geometric features and efficient establishment of geometric digital twin using density-based clustering, fitting and region growing algorithm. Experimental tests were conducted to evaluate the accuracy, efficiency, and feasibility of the proposed method. The results demonstrate that the proposed approach not only robustly achieves high accuracy but also maintains high computational efficiency. Additionally, the geometric digital twin shows promise as tools for quantitatively assessing structural deformation and blockage defects.

Leveraging Machine Learning for Pipeline Condition Assessment

Pipeline condition assessment is a cost-effective method to determine the status of pipeline structure and predict failure probability. Although 100% inspection may not be feasible for decision makers, recent advancements in machine learning techniques have enabled more effective pipeline condition assessment. This paper provides a comprehensive review of machine learning applications in pipeline condition assessment, covering aspects such as fault diagnosis, risk prediction, parameter prediction, and visual defect recognition. The present study endeavors to make the following contributions: (1) extraction of the model, data size, and other relevant information from 91 papers, (2) in-depth analysis of the state of the art and frameworks of the models discussed in the 91 papers, (3) summary of the data characteristics, input variables, and accuracy of machine learning models, and (4) exploration of the potential avenues for future research in the use of machine learning for pipeline condition assessment. This review aims to serve as a practical reference for scholars engaged in related research. The review highlights the fact that the majority of the models employed in pipeline condition assessment are original, and the utilization of hybrid models remains limited. Transfer learning and reinforcement learning are identified as potential avenues for future research because they hold promise in facilitating the adaptive selection of model inputs and the transfer of models to similar projects. Furthermore, breaking down data barriers is deemed essential for advancing the use of machine learning in pipeline condition assessment.

Fatigue condition assessment of subsea pipelines under vortex induced vibration and cyclical lateral displacement

ABSTRACT Subsea pipeline may suffer from vortex-induced vibration (VIV) and cyclical lateral displacement (CLD), which can result in fatigue failure of subsea pipeline. This paper presents a fatigue damage assessment of subsea pipelines under VIV and CLD. A finite element model of pipeline considering the pipe–soil coupling action is developed, and dynamic response of subsea pipeline under VIV and CLD is implemented to obtain time history of equivalent stress. The integrated fatigue damage of subsea pipeline under VIV and CLD is estimated using Palmgren–Miner linearly cumulative fatigue damage theory. The effect of pipe diameter, span length, pipe length and tidal period on fatigue damage of subsea pipelines under CLD is analyzed. A case study of a subsea pipeline in the strong tide area is performed. The results show the fatigue life of pipeline under VIV and CLD is 14.1 years, which is 2.3 years lower than the pipeline under VIV alone. The study can support more accurate estimation of fatigue life of subsea pipelines.

Dynamic monitoring of leaking oil diffusion in porous media: An improved method assisting buried oil pipeline condition assessment

The environmental pollution caused by buried oil pipeline leakage is a typical non-aqueous phase liquid (NAPL) pollution accident. Therefore, it seems more reasonable to assess the condition of buried oil pipelines considering the potential contamination range. The law of oil diffusion in porous soil is very important for the safety management of buried pipelines and the remedy of leakage accidents. This paper proposes a dynamic observation technique of NAPL in porous media for buried oil pipeline leakage. A quantitative relationship between the diesel saturation and color components is established based on the theoretical analysis combined with 21 groups of dyed diesel experimental results. The accuracy of the observation technique is verified by a quantitative analysis of a series of discrete point samplings and a comparison of diesel volume in a diesel diffusion experiment. The results show that the combination of a specified saturation boundary and the color components S and I can effectively track the diesel diffusion fronts. The relative error between the calculated cumulative volume and the injected cumulative volume fluctuates around 5% at different times. This improved technology can effectively make up for the limitation of the traditional light transmission visualization (LTV) under low-intensity light and promote the study of NAPLs diffusion characteristics. Furthermore, the dynamic observation technique of NAPL in porous media for buried oil pipeline leakage monitoring can make a significant contribution to the improvement of the level of buried pipeline leakage accidents and remediation.

Transient analysis based pipeline condition assessment in looped water pipe networks

Transient analysis based pipeline condition assessment in looped water pipe networks

High resolution acoustic identification of clusters of small blockages in fluid-filled pipe using maximum likelihood estimation

This paper presents a method for identifying a cluster of small blockages (i.e., blockages with length on the order of centimeters and radial extent on the order of millimeters and separated by a distance on the order of few centimeters) in pressurized fluid-filled pipes using sound waves. This focus on defects with small scale, and, thus, small scattering strength is exploited to develop a Neumann series solution for the scattered acoustic wave field. The probing waves are such that the Helmholtz number (ratio of blockage longitudinal length scale and probing acoustic wavelength) is of order 1 or larger. A high resolution inverse technique for identifying a cluster of small blockages based on the maximum likelihood estimation principle is developed. The proposed technique uses two-dimensional search space to resolve each blockage in the cluster and requires a single measurement point only. The method is successfully tested through both numerical and laboratory experiments. The proposed methodology allows an early identification of a cluster of small defects and leads to reliable condition assessment of pipelines, which is necessary to inform decisions as to when remedial actions are required.

Pipeline condition assessment and finite element modeling of mechano-electrochemical interaction between corrosion defects with varied orientations on pipelines

Multiple corrosion defects located in varied orientations are common on pipelines, significantly impacting pipeline conditions. In this work, 3D finite element (FE) based multi-physics field coupling models were developed to model mechano-electrochemical (M−E) interaction between corrosion defects with varied orientations and its effect on pipeline conditions. The effects of the parameters, including defect geometries, operating conditions, and the relative positions and spacings between the defects, were determined. The results demonstrated that, the strongest M−E interaction occurred between defects when the longitudinal or circumferential spacing is 0, causing high-level local stress concentration and the anodic current density (i.e., corrosion rate) at the corrosion defects. As the defects gradually overlapped or separated from each other on the pipe surface, the magnitude of the M−E interaction decreased. The interaction between defects even disappeared when the longitudinal or circumferential spacing between defects reached 96 mm or 72 mm, respectively, and the defects can be assessed separately. An increased internal pressure led to local plasticity deformation and anodic current density concentration occurring at the inner edge of the defects and defect adjacent area. For example, the maximum anodic current density increased by 56.4% when the internal pressure increased from 12 MPa to 17 MPa. It was also observed that M−E interaction between defects disappeared if the defects were shorter than 48 mm or shallower than 8 mm. Sensitivity analysis demonstrated that the degree of M−E interaction was most sensitive to circumferential spacing, followed by defect depth, defect length, longitudinal spacing, and internal pressure.

Progress in Drainage Pipeline Condition Assessment and Deterioration Prediction Models

The condition of drainage pipes greatly affects the urban environment and human health. However, it is difficult to carry out economical and efficient pipeline investigation and evaluation due to the location and structure of drainage pipes. Herein, the four most-commonly used drainage pipeline evaluation standards have been synthesized and analyzed to summarize the deterioration and breakage patterns of drainage pipes. The common pipe breakage patterns are also summarized by integrating the literature and engineering experience. To systematically describe the condition of drainage pipes, a system of influencing factors for the condition of pipes, including physical, environmental, and operational factors, has been established, and the mechanism of action of each influencing factor has been summarized. Physical, statistical, and AI models and their corresponding representative models have been categorized, and the research progress of current mainstream drainage-pipe deterioration and breakage prediction models are reviewed in terms of their principles and progress in their application.

AWWA Water Science Author Spotlight

Having recently published an article in AWWA Water Science, Hongfang Lu answered questions from the publication's editor-in-chief, Kenneth L. Mercer, about the research. A Hybrid Model for Monthly Water Demand Prediction: A Case Study of Austin, Texas Hongfang Lu, John Matthews, and Shuai Han I am now an associate professor at Southeast University in China. My current research focuses on trenchless technology and buried pipeline health monitoring. My research has been going on for nearly a decade, and my projects are all about pipelines. Since I have a background in oil and gas engineering, my initial research was on oil and gas pipelines. During my PhD program, I also did some research on water pipelines. During my PhD program, I was involved in a Water Research Foundation project on water pipeline condition assessment. During that time, I did some research on water. In addition, trenchless technology is more widely used in the water field. During his summer vacation in 2018, Hongfang went to New York City with one of his teachers and her family. So far, three teachers have greatly influenced my career. In chronological order, the first one is Professor Xiaonan Wu from Southwest Petroleum University (Chengdu, China), who was my tutor at the master's degree stage. She told me to have the courage to challenge complex problems in research. The second is my PhD research advisor, Dr. Tom Iseley, a professor at Purdue University (West Lafayette, Ind.) and a leader in the trenchless industry. He told me to keep an open mind in research and be mindful of the intersections between disciplines. The third is my current team leader, Professor Zhao-Dong Xu from Southeast University. He often reminds us to combine theory with practice and do research that is meaningful to people. This research is related to water demand prediction using machine learning methods. The motivation is to establish a hydraulic calculation model of the water supply network through the prediction of water demand. Many buried pipelines worldwide are aging or about to enter the aging stage. Health monitoring and trenchless repair of pipelines are essential; therefore, my research direction is related to these two points. Personally, I am very interested in the structure of pipes. Although pipeline structure is relatively simple compared with other forms, it also involves many scientific problems. The outbreak of COVID-19 in 2019 has triggered a lot of thinking among scholars. But even before the onset of its public health problems, using limited samples to predict future conditions and then using those results to take public measures was an important challenge in research. In June 2021, Hongfang participated in the biennial badminton competition at Southeast University, Nanjing, China. I was born in Nanyang, Henan Province, China, a place with abundant oil resources at that time. So I have been relatively familiar with oil since I was a child. When I was in college, my major was oil and gas storage and transportation engineering. In studying, I was very interested in pipelines, the vehicle for oil and gas resource transportation. To learn more about Hongfang's research, visit the article, available online at https://doi.org/10.1002/aws2.1175.

Pipeline Condition Assessment by Instantaneous Frequency Response over Hydroinformatics Based Technique—An Experimental and Field Analysis

A common issue in water infrastructure is that it suffers from leakage. The hydroinformatics technique for recognizing the presence of leaks in the pipeline system by means of pressure transient analysis was briefly explored in this study. Various studies have been done of improvised leak detection methods, and Hilbert Huang Transform has the potential to overcome the concern. The HHT processing algorithm has been successfully proven through simulation and experimentally tested to evaluate the ability of pressure transient analysis to predict and locate the leakage in the pipeline system. However, HHT relies on the selection of the suitable IMF in the pre-processing phase which will determine the precision of the estimated leak location. This paper introduces a NIKAZ filter technique for automatic selector of Intrinsic Mode Function (IMF). A laboratory-scale experimental test platform was constructed with a 68-metre long Medium Polyethylene (MDPE) pipe with 63 mm in diameter used for this study and equipped with a circular orifice as an artificial leak in varying sizes with a system of 2 bar to 4 bar water pressure. The results showed that, although with a low ratio of signal-to-noise, the proposed method could be used as an automatic selector for Intrinsic Mode Function (IMF). Experimental tests showed the efficiency, and the work method was successful as an automatic selector of IMF. The proposed mathematical algorithm was then finally evaluated on field measurement tested on-site of a real pipeline system. The results recommended NIKAZ as an automatic selector of IMF to increase the degree of automation of HHT technique, subsequently enhancing the detection and identification of water pipeline leakage.

Automated Vision Systems for Condition Assessment of Sewer and Water Pipelines

Sewer networks and water distribution systems are among the most valuable and critical urban assets for a community. These systems have their respective time span to provide continuous service to the residents. Hence, it is paramount to assess the condition of sewer and water pipelines to ensure sustainable, reliable, and cost-effective transportation of sewerage and water supply. The assessment is usually done by the inspection robots, which are equipped with machine vision systems and/or sensors. The inspection robots acquire the inspection data, and the operators then conduct the survey of the captured video and/or sensory data to interpret the results. Nowadays, automated solutions are being adopted by industry to achieve an accurate and efficient assessment. This article surveys the state of the art of the automated vision systems, which are employed for condition assessment of sewer and water pipelines, and identifies the challenges for future research. The following areas are highlighted in this survey: 1) the typical types of the faults and failures, which include the concept and definition of the sewer and water pipelines; 2) the inspection systems, e.g., robotic platforms for sewer and water pipeline inspection; 3) the machine vision systems for fault detection; 4) the computational frameworks for condition assessment; and 5) the challenges and suggestions for future research. This article summarizes the current state of automation in the machine vision technology for condition assessment (both hardware and software perspectives) of sewer and water pipelines and also provides a reference for researchers to further advance the technology in this field. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —The motivation behind this article was the current challenges and the open issues of the vision system for the condition assessment of sewer and water pipelines. This article presents detailed reviews of the state-of-the-art of the vision system (both hardware and software) and discusses the existing problems. This survey aims to help engineers and researchers to resolve and improve the problems and extend the field of the existing automated frameworks. This article is organized in the form of a survey so that researchers can benefit and get all the useful information at a glance. This article provides a rigorous overview of typical faults, inspection robots, visual techniques, and automated frameworks for the condition assessment and discusses the challenges and future research directions. The objective of this article is to create a baseline for the readers to acquaint themselves with the state of the art and advance the research in this field.

Valve Detection for Autonomous Water Pipeline Inspection Platform

Water distribution and transmission lines are indispensable to urban infrastructure. The water pipelines are subject to both structural and functional deterioration due to various reasons including aging, negligence, and high demand for water supply. Hence, to ensure a safe and reliable water supply, the water utilities need to perform routine pipe condition assessment. The condition assessment is usually carried out by visual inspection with the machine vision system carried by a robotic platform. The inspection platforms will capture the internal condition of the water pipelines in a video stream. However, the robotic platform frequently experiences difficulties while traversing through the valves installed along the pipeline. This inhibits and disrupts the inspection process of the water pipelines. Therefore, this article proposes a deep learning-based automatic valve detection framework to facilitate the robot’s navigation and ensure continuous inspection without any interruptions. The valve detection model is developed by combining MobileNet-160 and Feature Pyramid Network and is named as MFPN. The developed framework also employs a generative adversarial network to solve the sparse dataset issues and improve the generality of the framework. The comparative study and ablation analyses demonstrate that the proposed framework can achieve a higher mAP value of 89.11% in comparison with the state of the art. Hence, this light weight and efficient solution can be deployed to the robotic platform for real-time valve detection and enable autonomous navigation of the robotic platform for condition assessment of water pipelines.

Fast computation of inverse transient analysis for pipeline condition assessment via surrogate modeling with sparse sampling strategy

Inverse transient analysis (ITA) is a powerful tool for pipeline condition assessment, where the concerned parameters are decided by matching the measured transient signal with its physical model. This scheme is computationally expensive because, in the optimization procedure, the model has to be evaluated many times via time-domain full-wave numerical simulations. To address this problem, a fast algorithm of ITA is proposed, where the role of the numerical model is replaced by an easy-to-evaluate surrogate model. The Kriging method is employed to build the surrogate model of transient wave, given a series of samples of concerned parameters and the associated numerical model outputs. Considering the sparse nature of the high-dimensional pipe condition parameters, a sparse sampling strategy is proposed to optimize the samples of parameters. Two application examples, i.e., pipe viscoelastic parameter estimation and leakage identification, are introduced to demonstrate that the proposed method is as accurate as the traditional ITA but has a much lower computational cost.

A steerable pyramid autoencoder based framework for anomaly frame detection of water pipeline CCTV inspection

Closed-circuit television (CCTV) is being widely adopted in water pipeline inspection. The inspector needs to spend a long time to watch the recorded video during the office-based survey and can get fatigue easily. An automated process can release the inspector’s work load and ensure the consistent quality of the survey. However, a fully automated survey of varied structural discontinuities still remains as a challenge. This study aims to first identify the anomaly frames of the CCTV video, which contain the major anomalies captured from the internal surface of the pipe. Thus, the inspector can focus more on these anomaly frames. In this paper, an anomaly frame detection framework based on steerable pyramid autoencoder (SPAE) is proposed. The SPAE can generate discriminative representations to be used in the prediction. Both the parameter optimization and comparative studies for the proposed SPAE were carried out in this research. The experimental results demonstrate that this novel SPAE algorithm can achieve 0.984 accuracy and 0.984 F1-score, which outperforms other state-of-the-art methods selected for comparison. Thus, the proposed framework can significantly improve the accuracy and efficiency for anomaly frame detection, which will highly facilitate the pipeline condition assessment through the CCTV inspection.

Prediction of Breaks in Municipal Drinking Water Linear Assets

Improper asset management practices increase the probability of water main failures due to inactive intervention actions. The annual number of breaks of each pipe segment is known as one of the most important criteria for the condition assessment of water pipelines. This metric is also considered one of the major performance measures in levels of service (LoS) studies. In an effort to maximize the benefits of historical data, this research utilized the evolutionary polynomial regression (EPR) method in determining the best mathematical expression for predicting water pipeline failures. The prediction model was trained and tested on the city of Montreal water network. After determining the best independent variables through the best subset regression, pipelines were clustered based on their attributes (length, diameter, age, and material). The majority of the models provided high R2 values, but the highest performing model’s R2 was 89.35%. Further, a sensitivity analysis was also performed and showed that the most sensitive parameter was the diameter, and the most sensitive material type to age was ferrous material. The tools and stages performed in this research showed promising results in predicting the expected water main failures using four different asset attributes. Therefore, this research can be implemented in asset management best practices and in LoS performance measures to predict the number of water pipeline failures. To further improve the prediction model, additional explanatory variables could be considered along with leveraging multiple artificial intelligence tools.

A novel pipeline leak detection approach independent of prior failure information

Condition monitoring of pipelines is of importance to detect fluids leakage and associated financial losses and accidents. Artificial intelligence (AI) techniques have been widely used for the pipeline condition assessment. A major limitation of the currently-used supervised AI methods is that they heavily rely on sufficient pipeline failure historical data for their training. To cope with this issue, this paper proposes a health index-oriented approach based on multiscale analysis, Kolmogorov-Smirnov (KS) test, and Gaussian mixture model (GMM) for determining the leakage situation in pipelines. GMM is an unsupervised AI method capable of training itself with pipeline normal condition data. In this study, acoustic emission (AE) signals are first acquired from the pipeline at different pressure conditions. Then, the multiscale analysis and KS test are deployed to extract suitable features from the AE signals. The feature samples corresponding to the pipeline normal condition are used to train the GMM. Finally, the feature samples to be tested are supplied to the GMM and the desired health indicator is obtained. The results confirm the effectiveness of the proposed approach in discriminating the normal and leak conditions as well as the severity of leaks. Further, the GMM classifier trained with features derived from multiscale analysis and the KS test outperforms the GMM trained with crest factor, and mean frequency.

Bayesian Inverse Transient Analysis for Pipeline Condition Assessment: Parameter Estimation and Uncertainty Quantification

Strategic pipeline asset management requires accurate and up-to-date information on pipeline condition. As a tool for pipeline condition assessment, inverse transient analysis (ITA - a pipeline model calibration approach) is typically formulated as a deterministic problem, and optimization methods are used for searching a single best solution. The uncertainty associated with the single best solution is rarely assessed. In this paper, the pipeline model calibration problem is formulated as a Bayesian inverse problem, and a Markov Chain Monte Carlo (MCMC) based method is used to construct the estimated posterior probability density function (PDF) of the calibration parameters. The MCMC based method is able to achieve parameter estimation and uncertainty assessment in a single run, which is confirmed by numerical experiments. The proposed technique is also validated using measured hydraulic transient response data from an experimental laboratory pipeline system. Two thinner-walled pipe sections (simulating extended deterioration) are successfully identified with an assessment of the parameter uncertainty. The results also suggest that proper sensor placement can reduce parameter uncertainty and significantly enhance system identifiability.