Pipe Inspection Research Articles

Image restoration of reflectors by the method of digital aperture focusing in thick-walled pipes of small diameter

In ultrasonic inspection of pipes of various diameters using antenna arrays and arrays, two technologies of reflector image reconstruction are widely used: antenna array focusing technology (PAUT) and digital aperture focusing (DAF) technology. If the tube diameter is larger than a hundred wavelengths, the DAF method can be used to reconstruct the reflector image by taking into account several reflections from the boundaries, assuming that the object of inspection is flat. In this case, the errors in the formation of DAF-image of reflectors will be insignificant. However, if the pipe diameter is several tens of wavelengths and the wall thickness is about half of the pipe diameter, then in this case the geometry of the inspection object must be taken into account to obtain a high-quality DAF-image of the reflectors. The paper considers the peculiarities of image formation at registration of echo signals by an antenna array or matrix, when scanning both on the external and internal surfaces of the object of control. In numerical and modeling experiments it is shown that both antenna array and antenna array can be used to obtain high-quality DAF-image of reflectors when scanning along the outer surface of a thick-walled pipe of small diameter. This is due to the presence of the effect of physical focusing of the ultrasonic field. But when scanning along the inner surface of a thick-walled tube of small diameter, because of the defocusing effect, it is necessary to register echoes with an antenna array to restore the image of reflectors.

Enhancing Inspection Tasks: A Dataset for Corrosion Defects in Pipelines

Inspection plays a crucial role in ensuring the longevity, security, and dependability of critical public infrastructure for both governments and businesses. However, traditional inspection processes are often labor-intensive and pose various risks. Consequently, there is a growing need for automation in such tasks. This research paper presents a comprehensive dataset that can be utilized to develop algorithms and systems for automating the inspection process, a critical area in the field of computer vision. The dataset encompasses a diverse range of inspection scenarios and serves as a valuable resource for advancing automation technology specifically for the inspection of steel pipes to detect corrosion defects. Real-life pipe maps have been used to derive scenarios that represent varying levels of corrosion. By leveraging this dataset, researchers and practitioners can contribute to the development of more efficient and accurate automated inspection systems, thus greatly improving the overall efficiency and long-term safety of infrastructure inspection.

FalconScan: A hybrid UAV-crawler system for NDT inspection of elevated pipes in industrial plants

Periodic non-destructive testing (NDT) of pipes and tanks is vital in industrial plants, such as Oil & Gas facilities, to proactively detect defects and corrosion before leaks and forced shutdowns occur. This paper presents a hybrid system, consisting of a UAV and a crawler, which enables detailed contact-based inspection of elevated pipes, in pursuit of eliminating the need for dangerous scaffolding and manual inspection to improve safety and reduce cost. Similar to avian animals, the UAV autonomously perches on the pipe to conserve energy. A small inspection crawling robot is carried by the UAV, and is subsequently released onto the pipe’s surface to inspect its health. The crawler uses magnetic wheels for agile mobility and houses an ultrasonic testing (UT) sensor to thoroughly scan the pipe and detect wall thinning, which is a precursor for leaks. Finally, the crawler re-docks with the UAV, which in turn detaches from the pipe to fly back home or inspect another pipe. The multi-robot system is designed for and tested on pipe diameters as small as 8 in.

Design and analysis of a flexible struts V-expander tensegrity robot for navigating pipes

Tensegrity robots have increasingly attracted attention in recent years. Traditionally, these robots rely on rigid struts and cables to maintain equilibrium configurations. However, the inflexibility inherent in these rigid struts curtails the robot’s capacity for deformation, thereby amplifying structural intricacy and imposing limitations on potential applications, particularly in the realm of pipe inspection. Drawing inspiration from the V-expander tensegrity structure, this paper presents a design, analysis, and validation of a flexible struts tensegrity robot. The integration of flexible struts enables the robot to exhibit a compact structure, passive compliance, and excellent adaptability. Through the actuation of three active cables, the robot exhibits inchworm-like motion capabilities for pipes ranging from 50 mm to 110 mm in diameters. A kinetostatics modeling approach is presented to predict the shapes of flexible struts and control the motion behaviors of the robot. To validate the capabilities of the proposed robot and assess the effectiveness of the kinetostatics model, a prototype was constructed and subjected to a series of experiments. The results demonstrate that the prototype exhibits remarkable shape changeability, mobility, and adaptability, while precisely controlling the contact force.

Inspection prioritization of gravity sanitary sewer systems using supervised machine learning algorithms

Underground wastewater collection systems degrade with time, necessitating utility owners to engage in ongoing evaluations and enhancements of their asset management frameworks to preserve the performance of their assets. The inspection and condition assessment of sewer pipes are crucial for the effective operation and maintenance of sewer systems. The closed-circuit television (CCTV) is frequently employed to examine sewer pipes in the United States. This procedure is both costly and laborious because of the extensive number of pipes in a metropolis. Prioritisation of inspection for sanitary sewage pipe segments requiring repair or maintenance can be done in advance depending on their past performance. Hence, the aim of this study is to construct a predictive model for the state of sanitary sewer pipes, utilising data collected from a city located in the southcentral region of the United States. The main contribution is that this study used multiclass classification and predicted PACP scores of the pipes. Condition prediction models were developed using extensively utilised supervised machine learning algorithms including logistic regression (LR), k-nearest neighbors (k-NN), and random forest (RF). However, the bulk of the constructed models were assessed using a limited number of assessment measures, such as the receiver operator characteristic (ROC) curve and the area under the curve (AUC) value. This paper asserts that the assessment of the predictive capacity of these models cannot be determined only by relying on ROC and AUC values. Out of the three models evaluated in this study, the LR model had an AUC value of 0.76. However, this model had a higher number of misclassifications or inaccurate predictions compared to the other models. Consequently, these models were assessed using additional assessment measures, including precision, recall, and F-1 scores (which represent the harmonic mean of precision and recall). Curiously, the LR model achieved an F1-score of 0.28 on a scale ranging from 0 to 1. The RF model yielded an F1-score of 0.45 and an AUC value of 0.86. The existing model can be enhanced before it is employed by asset managers during the inspection phase to assess the state of their sanitary sewers and identify essential sewers that require immediate care.

An application of a beamforming technique, linear acoustic array and robot for pipe condition localization

Pipe inspection robots with sensors significantly enhance the maintenance of water systems by enabling early defect detection and reducing the risks of leaks and environmental damage. This paper introduces a sparse representation acoustic beamformer designed to locate artefacts within pipes using a short linear microphone array on a robot. The primary contributions include: (i) a new acoustic beamforming approach based on sparse representation that accurately predicts pipe conditions with a localization error within 3 % of the sensing distance; (ii) an enhanced beamforming algorithm combining plane wave and first non-axisymmetric mode, extending the frequency range from < 1300 Hz to < 2200 Hz in a 150 mm diameter pipe (an increase by 1.69 times), effectively managing unwanted acoustic reverberations and distinguishing blockages from lateral connections; and (iii) a novel robust algorithm predicting pipe length with an error margin within 2 %. This research advances acoustic sensing technologies for autonomous mobile robots inspecting buried pipes.

Worm-Inspired, Untethered, Soft Crawling Robots for Pipe Inspections.

The increasing demand for inspection, upkeep, and repair of pipeline and tunnel infrastructures has catalyzed research into the creation of robots with superior flexibility, adaptability, and load-bearing capacities. This study introduces an autonomous soft robot designed for navigating both straight and curved pipelines of 90 mm diameter. The soft robot is enabled by an elongation pneumatic actuator (EPA) as its body and multiple radial expansion pneumatic actuators (REPAs) as its feet to provide adhesion and support on the pipe walls. It achieves a horizontal movement speed of 1.27 mm/s and ascends vertically at 0.39 mm/s. An integrated control mechanism, merging both pneumatic and electrical systems is employed to facilitate unrestrained movement. A novel control tactic has been formulated to ensure synchronized coordination between the robot's body deformation and leg anchoring, ensuring stable movement. This soft robot demonstrates remarkable mobility metrics, boasting an anchoring strength of over 100 N, a propelling force of 43.8 N when moving vertically, and a pulling strength of 31.4 N during navigation in curved pipelines. It can carry a camera to capture the internal view of the pipe and remove obstacles autonomously. The unconstrained and autonomous movement of the untethered soft robot presents new opportunities for various applications at different scales.

Gauging and Imaging of Pipes using Water-Immersible Ultrasonic Instrumentation System

Abstract The purpose of this research work is to establish the functionality of the novel ultrasonic non-destructive inspection system and accurate gauging of pipes and to locate and visualise flaws in the form of B-Scan cross-sectional view (front-view) of the pipe under test. In this paper, presents a custom-made perspex inspection head assembly integrated with a stand-alone, Li-ion battery-powered and IP67-grade water-immersible ultrasonic instrumentation and gauging system, which enables an efficient assessment of the condition and health of pipes in stringent environments. Extensive inspection was carried out on six samples of 12&amp;#x94; Inner Diameter (ID) type carbon steel (CS) pipes with length of 500 mm and having machined wall thickness to simulate loss of wall-thicknesses from 10 % over a length 150 mm of pipe, using 5 MHz spherically focused transducers. Further inspection were carried out on a 12&amp;#x94; CS pipe with four notches and four flat bottom holes (FBH) machined on the OD side. Identical flaws were also machined onto 12&amp;#x94; CS pipe of total length of 700 mm containing water inside the pipe in flowing condition with water flow rate of 100 litres per minute (LPM). The test results demonstrate the effectiveness of the developed IP67 grade water-immersible ultrasonic pipe inspection and gauging instrumentation system for assessing the condition and health of long-length carbon steel pipes operating in harsh environments.

Design of guided wave magnetostrictive patch transducer with vertical static bias magnetic field for pipe inspection

Compared with electromagnetic acoustic transducer (EMAT), magnetostrictive patch transducer (MPT) has higher energy conversion efficiency. MPT is often used to excite and receive long-distance ultrasonic guided waves for defect detection or structural health monitoring of large industrial equipment. In this work, a new design method of permanent magnet MPT using the vertical static bias magnetic field to excite shear horizontal (SH) guided waves is proposed. First, the mathematical model of exciting ultrasonic guided waves by a vertical static bias magnetic field MPT is established, and the equivalent surface stress in the magnetostrictive patch is calculated. It is shown that the vertical static bias magnetic field MPT can excite SH guided waves in plates. Then, the influence of static bias magnetic field intensity on the amplitude of guided wave signal is analyzed. Compared with other traditional MPTs, the amplitude of SH guided wave signal excited by the vertical static bias magnetic field MPT is increased. Finally, a periodic permanent magnet (PPM)-MPT with a central frequency of 120 kHz is designed. The proposed PPM-MPT improves the energy conversion efficiency compared to the circumferential alternating permanent magnet array type MPT. The PPM-MPT can detect defects in a steel pipe with a wall thickness of 3.5 mm and an inner diameter of 100 mm. The results show that the PPM-MPT designed in this paper successfully detects the through hole with a diameter of 2 mm, and the cross-section loss rate of the defect is about 0.6 %.

Autonomous Robot On-Pipe Leak Detection

The research addresses the critical need for efficient and cost-effective pipe inspection methods in modern infrastructure, focusing on the maintenance of water and gas delivery systems. The objective is to improve defect detection and maintenance processes in pipes, reducing downtime and operational costs. The proposed method involves deploying pipe inspection robots, with an emphasis on on-pipe robots tailored for specific applications such as urban gas lines, small feeder pipes, and sewer systems. These robots are categorized into seven sub-types, including wheel-type, inchworm-style, AI-powered sewer inspection, caterpillar-style, and helical motion robots, each designed for unique inspection challenges. A newly developed versatile on-pipe inspection robot is highlighted for its ability to inspect a variety of plastic and metal pipes in both horizontal and vertical orientations. This robot features a body, foreleg system, rear leg system, and springs, enabling smooth navigation through pipes. The performance of this robot shows substantial improvements in inspection efficiency and accuracy, proving its effectiveness as a modern solution for infrastructure maintenance.

Design and Static Equilibrium Analysis of a Modular Pipe Inspection Robot

Design and Static Equilibrium Analysis of a Modular Pipe Inspection Robot

Prediction of failures in sewer networks using various machine learning classifiers

ABSTRACT In sewer networks, failure prediction plays a significant role in operation and maintenance plans of wastewater utilities. This study aims to determine the effective variables on the failures by using feature selection algorithms (FS) and achieve maximum model accuracy with minimum variables. Also, four scenarios based on the suggested FS algorithms were developed. In these scenarios, the best prediction models were investigated using machine learning classifiers (ML) such as neural network classifier (NNC), gradient boosting machine (GBM), random forest (FR), and hybrid model (HM). The classification performance of ML models was evaluated using accuracy, precision, F1_score, and receiver operating characteristics (ROC) curve. The model accuracies ranging from 0.99 for accuracy to 1 for the ROC curve were achieved through ML algorithms. In conclusion, the ML algorithms suggested in this study may be a decision support tool for wastewater utilities in prioritizing the replacement, maintenance, and inspection of sewer pipes.

Residual Magnetic Field Testing System with Tunneling Magneto-Resistive Arrays for Crack Inspection in Ferromagnetic Pipes.

Ferromagnetic pipes are widely used in the oil and gas industry. They are subject to cracks due to corrosion, pressure, and fatigue. It is significant to detect cracks for the safety of pipes. A residual magnetic field testing (RMFT) system is developed for crack detection in ferromagnetic pipes. Based on this background, a detection probe based on an array of tunneling magneto-resistive (TMR) sensors and permanent magnets is exploited. The probe is able to partially magnetize the pipe wall and collect magnetic signals simultaneously. First, a theoretical analysis of RMFT is presented. The physics principle of RMFT is introduced, and a finite element model is built. In the finite element simulations, the effects of the crack length and depth on the RMFT signal are analyzed, and the signal characteristics are selected to represent the crack size. Next, the validated experiments are conducted to demonstrate the feasibility of the proposed RMFT method in this paper.

Modal coupling analysis of the acoustic wave scattering from blockage in a pipe

Acoustic sensing system deployed on an autonomous platform (also referred to as robot) for accurate condition monitoring and fault detection in pipes requires the knowledge of wave scattering from various in-pipe faults or the robot itself. Existing solutions to estimate wave scattering tend to either be constrained to the plane wave regime or be computationally expensive outside this range. There has been a lack of work to apply analytical modal coupling methods to study wave scattering from a non-symmetric cross-sectional change in a pipe beyond the plane wave regime. This paper proposes an efficient three-dimensional (3D) modal coupling method to predict wave scattering from a cross-sectional change in a pipe in the frequency range beyond the plane wave regime. The trapped modes induced by a 3D axisymmetric or non-axisymmetric cross-sectional change in an air-filled pipe are estimated using modal coupling analysis. The derived analytical model is validated against numerical simulations and measurements. It agrees with a finite element simulation with Comsol Multiphysics in the 0.01<kR<4 frequency range (k being the wavenumber and R being the pipe radius) within 15 %, but it is approximately 600 times faster than the Comsol simulation making it attractive for the deployment on sensors with limited computer power that can be used for autonomous inspection of buried pipes.

Advancing illicit connection diagnosis of urban stormwater pipes: Comprehensive analysis with EEM fluorescence spectroscopy

Urban drainage systems are significant contributors to the issue of black-odorous water bodies. The current application of stormwater pipe inspection technologies faces substantial limitations, especially in industrial areas with diverse wastewater. This study introduced an innovative approach using excitation-emission matrix (EEM) fluorescence spectroscopy for rapid and accurate diagnosis, providing a new perspective for diagnosing illicit connections. In single wastewater-type areas like residential zones, the method achieved a remarkable 91.5 % accuracy solely through spectra observation and fluorescence peak intensity comparison, outperforming conventional NH3-N-based techniques, which reached an accuracy of only 68.1 %. For regions with complex wastewater scenarios, after EEM subtraction, the residual spectra can be roughly categorized into four distinctive categories based on characteristics. This provides a preliminary assessment and helps in initially identifying the types and sources of inflowing wastewater. Furthermore, the least squares (LS) method refines diagnosis results, offering calculated coefficients reflecting the probability and severity of suspected wastewater intrusion. Simulation experiments and field sample analyses validated the feasibility and accuracy of the EEM-based method, highlighting its advantages for diagnosing illicit connections in both single and mixed wastewater scenarios. The results can significantly narrow down the investigation scope and enhance the confirmation of wastewater sources, exhibiting promising application prospects.

Pipe Crawler Robot

This pipe inspection robot aims at detecting the exact location of leakage and clearing the blockages, thus removing human factors from labor-intensive and dangerous work, thereby reducing the number of accidents that happen due to the lack of regular inspection. Detection Pipe leakage, water distribution system. An autonomous pipe crawler robot is designed to navigate through pipelines, offering a practical and efficient solution for inspecting and monitoring the condition of pipe interiors. This robot leverages advanced locomotion mechanisms and control strategies to maneuver through various pipe diameters and configurations, including straight sections, bends, and junctions. Equipped with sensors and cameras, it can detect anomalies such as cracks, corrosion, blockages, and leaks, providing real-time, high-resolution imagery and data. This technology significantly reduces the need for manual inspections, minimizes human exposure to hazardous environments, and improves the precision and efficiency of pipeline maintenance operations. The development of pipe crawler robots represents a significant advancement in the field of robotics and infrastructure management, promising enhanced safety, reliability, and cost-effectiveness for industries reliant on extensive pipeline networks. Key words: inspection, autonomous, locomotion, robot, crawler., etc.

Generation of bidirectional shear horizontal guided waves by size optimization of face-shear piezoelectric transducers

Generating directional guided waves helps to avoid unwanted reflections while providing location information for defects. Bidirectional waves focus wave beams in two opposite directions, which can be applied for generating circumferential zero-order shear horizontal (SH0) waves for large-diameter pipe inspection. In this paper, a novel method for generating bidirectional SH0 waves through size optimization using face-shear (d24) lead zirconate titanate (PZT) transducer is proposed. A theoretical model is established based on the shear-lag model and the Huygens principle, which describes the generation and propagation of SH0 waves in thin plate by bonded d24 PZT transducer. The theoretical model is validated by multi-physics finite element (FE) simulation results in terms of SH0 displacement wavefield. Based on the theoretical model, the performance of bidirectional SH0 wave generation is studied. The maximum bidirectional ratio that can be achieved by d24 PZT within a size range (1–25 mm) under a five-cycle sinusoid tone-burst excitation at a center frequency range (50–300 kHz) is studied. Results show that over 20 dB effect can be achieved at the PZT with a specific aspect ratio. Laboratory experiments are conducted using d24 PZT transducers at 50–300 kHz to validate the effect of bidirectional wave generation in practical detection scenarios. Experimental results show effects of over 20 dB within a certain frequency range can be achieved when using a PZT with a large aspect ratio, which agree well with the theoretical predictions.

An Innovative Pipe Inspection Tool Utilizing Electromagnetic Resonance Coupling and Machine Learning

This paper describes an advanced tool that uses electromagnetic resonance coupling and machine learning techniques to detect and characterize metal loss on the inner surface of a metallic pipe. The proposed tool uses a transmitter coil placed along the axis of the pipe and four sensor coils installed around the transmitter coil. Any defect on the pipe surface leads to changes in the impedance of the transmitter and sensor coils as well as in the mutual coupling between them, thus creating a detectable variation in the outputs of one or multiple sensor coils. An artificial neural network is developed to reconstruct two-dimensional pipe cross sections and to completely characterize the defects using these variations. The proposed tool is tested and validated via simulations and data collected using an experimental prototype. Results show that the tool can fully characterize the size, location (azimuthal angle), and level (thickness) of metal loss.

Pipe Inspection Robot

Abstract: A pipe inspection robot is device that is inserted into pipes to check for obstruction or damage. These robots are traditionally manufactured offshore, are extremely expensive, and are often not adequately supported in the event or malfunction. This had resulted in associated environmental services limited. A New Zealand utilize of this equipment, facing significant periods of down time as they wait for their robots to be the repaired. Recently, they were informing that several robots were no longer supported. This project was conceived to redesign the electronics control systems one of these PIR, utilizing the existing mechanical platform. Requirements for the robot were that it must operate reliably in confined, dark and wet environments and provides a human wear with a digital video feed of the internal status of the pipes. There robot should as much as possible incorporate off the shaft components, cheap, and potentially onsite repair. This project details the redesign and constructions of such robots. Its employees their electronic boards integrated with mechanical components and provides video feedback via custom graphical interface although at the prototypes state the electronics has been successful with cost of less than a length of the original robot purchase prize

Development of mobile minirobots for in pipe inspection tasks

The possibilities to use a miniaturized robotic system for inspection, exploration and maintenance of the pipes are highlighted. In this paper, the authors’ contributionin this field is discussed. Two in-pipe minirobots developed by the authors are described. The efforts are focused on the in-pipe mobility and corresponding actuationsystems. Several functional characteristics (e.g. the maximum /minimum inner pipe diameter) are given.