Nondestructive Evaluation Inspection Research Articles

A framework for computing directivities for ultrasonic sources in generally anisotropic, multi-layered media

The knowledge of the directivity of ultrasonic sources is an important element in the design of non-destructive evaluation inspection. The commonly used directivity model of piezoelectric devices is the superposition of out-of-plane force monopoles on the stress-free surface of an isotropic elastic half-space. However, this does not cover a growing range of ultrasonic generation scenarios, as new ultrasonic technologies such as laser ultrasound (LU) emerges, and the need for inspection of materials such as carbon fibre reinforced polymers (CFRPs) rises. A directivity calculation framework based on the reciprocity theorem is developed in the current paper, which accommodates various combinations of source types, material properties, and boundary conditions, for example, a localised heating under a surface layer over an anisotropic half-space. The calculation is applicable to materials with weak anisotropy or wave modes that do not have cuspoidal wavefronts, and was validated with finite element models on key representative cases. The analytical solution for LU excitation in a CFRP sample also shows a good fit to the experimentally measured directivity of the quasi-longitudinal wave mode.

Creep Life Assessment Method for Super304H Boiler Tubes Based on HTS-SQUID Gradiometer

This paper presents a novel method for the creep life assessment of Super304H (KA-SUS304J1HTB, ASME code: 2328) boiler tubes based on a discrete-type eddy-current-testing (ECT) HTS-SQUID nondestructive evaluation (NDE) system. Six specimens were subjected to internal pressure creep tests at a stress level of 110 MPa and a temperature of 700°C. The specimens were inspected at various creep test times using our NDE system, which included an ECT probe and a HTS-SQUID gradiometer. By studying the physical characteristics and microstructures of the specimens at different test times, we found that the electrical conductivity and permeability of the specimens varied with an increasing test time. The detected signals may have been generated from creep voids and/or surface microcracks. Based on the NDE inspection results, creep life assessment curves that can be used for assessing the remaining creep life of the Super304H boiler tubes were plotted. The results confirmed the possibility of assessing the remaining creep life using our HTS-SQUID-based system in a nondestructive manner.

Leaky wave characterisation using spectral methods.

Leaky waves are an important class of waves, particularly for guiding waves along structures embedded within another medium; a mismatch in wavespeeds often leads to leakage of energy from the waveguide, or interface, into the medium, which consequently attenuates the guided wave. The accurate and efficient identification of theoretical solutions for leaky waves is a key requirement for the choices of modes and frequencies required for non-destructive evaluation inspection techniques. We choose a typical situation to study: an elastic waveguide with a fluid on either side. Historically, leaky waves are identified via root-finding methods that have issues with conditioning, or numerical methods that struggle with the exponential growth of solutions at infinity. By building upon a spectral collocation method, we show how it can be adjusted to find exponentially growing solutions, i.e., leaky waves, leading to an accurate, fast, and efficient identification of their dispersion properties. The key concept required is a mapping, in the fluid region, that allows for exponential growth of the physical solution at infinity, whilst the mapped numerical setting decays. We illustrate this by studying leaky Lamb waves in an elastic waveguide immersed between two different fluids and verify this using the commercially available software disperse.

Uncertainty Quantification for Deep Learning in Ultrasonic Crack Characterization.

Deep learning for nondestructive evaluation (NDE) has received a lot of attention in recent years for its potential ability to provide human level data analysis. However, little research into quantifying the uncertainty of its predictions has been done. Uncertainty quantification (UQ) is essential for qualifying NDE inspections and building trust in their predictions. Therefore, this article aims to demonstrate how UQ can best be achieved for deep learning in the context of crack sizing for inline pipe inspection. A convolutional neural network architecture is used to size surface breaking defects from plane wave imaging (PWI) images with two modern UQ methods: deep ensembles and Monte Carlo dropout. The network is trained using PWI images of surface breaking defects simulated with a hybrid finite element / ray-based model. Successful UQ is judged by calibration and anomaly detection, which refer to whether in-domain model error is proportional to uncertainty and if out of training domain data is assigned high uncertainty. Calibration is tested using simulated and experimental images of surface breaking cracks, while anomaly detection is tested using experimental side-drilled holes and simulated embedded cracks. Monte Carlo dropout demonstrates poor uncertainty quantification with little separation between in and out-of-distribution data and a weak linear fit ( R=0.84 ) between experimental root-mean-square-error and uncertainty. Deep ensembles improve upon Monte Carlo dropout in both calibration ( R=0.95 ) and anomaly detection. Adding spectral normalization and residual connections to deep ensembles slightly improves calibration ( R=0.98 ) and significantly improves the reliability of assigning high uncertainty to out-of-distribution samples.

Robust defect detection under uncertainties using spatially adaptive capacitive imaging

We develop a high-Q capacitive sensing based robust non-destructive evaluation (NDE) methodology that can be widely used in varied NDE applications. We show that the proposed method can detect defects in a host of robust regimes where uncertainties such as lift-off, probe tilt, fluctuations in sampling rates, and step sizes are inherent in the data collection process. We explicitly characterize the corruption in the capacitive sensing data due to various lift-off based uncertainties. We use a Bayesian decision theoretic approach to rigorously understand the impact of these corruptions on defect identification efficacy. Using an optimally tuned weighted classification loss, we prove that it is theoretically feasible to accurately detect defect location and sizes from capacitive sensing signals collected under the aforementioned uncertainties. The Bayesian decision theoretic study needs prior information for accurate detection that is not available in real NDE inspections. So, we develop a solely data driven algorithm that analyzes the capacitive sensing signals without any prior knowledge of defect or uncertainty types. The developed algorithm is non-parametric and uses spatially adaptive denoising to weed out uncertainty induced noises. By leveraging the spatial association in the capacitive sensing signals, our algorithm greatly improves on popular non-spatial approaches. Compared to popular thresholding methods and low-rank based denoising approaches, we demonstrate superior performance of the proposed method in terms of coverage and false positive metrics for defect identification. Using spatially adaptive denoising, we design a robust capacitive sensing method that can detect defects with high precision under various uncertainty regimes.

Collaborative Robotic Wire + Arc Additive Manufacture and Sensor-Enabled In-Process Ultrasonic Non-Destructive Evaluation.

The demand for cost-efficient manufacturing of complex metal components has driven research for metal Additive Manufacturing (AM) such as Wire + Arc Additive Manufacturing (WAAM). WAAM enables automated, time- and material-efficient manufacturing of metal parts. To strengthen these benefits, the demand for robotically deployed in-process Non-Destructive Evaluation (NDE) has risen, aiming to replace current manually deployed inspection techniques after completion of the part. This work presents a synchronized multi-robot WAAM and NDE cell aiming to achieve (1) defect detection in-process, (2) enable possible in-process repair and (3) prevent costly scrappage or rework of completed defective builds. The deployment of the NDE during a deposition process is achieved through real-time position control of robots based on sensor input. A novel high-temperature capable, dry-coupled phased array ultrasound transducer (PAUT) roller-probe device is used for the NDE inspection. The dry-coupled sensor is tailored for coupling with an as-built high-temperature WAAM surface at an applied force and speed. The demonstration of the novel ultrasound in-process defect detection approach, presented in this paper, was performed on a titanium WAAM straight sample containing an intentionally embedded tungsten tube reflectors with an internal diameter of 1.0 mm. The ultrasound data were acquired after a pre-specified layer, in-process, employing the Full Matrix Capture (FMC) technique for subsequent post-processing using the adaptive Total Focusing Method (TFM) imaging algorithm assisted by a surface reconstruction algorithm based on the Synthetic Aperture Focusing Technique (SAFT). The presented results show a sufficient signal-to-noise ratio. Therefore, a potential for early defect detection is achieved, directly strengthening the benefits of the AM process by enabling a possible in-process repair.

A Framework of Using Customized LIDAR to Localize Robot for Nuclear Reactor Inspections

While remote inspection of industrial structures, such as nuclear reactors, using robotic crawlers currently presents significant advantages in terms of safety, accuracy and cost, other challenges emerge due to poor context-awareness and positional accuracy. This results in a lack of visibility for path planning and difficulty in precise localization of NDE (Non-Destructive Evaluation) inspection data. LIDAR (Light Detection and Ranging) are one form of sensors that estimate distances at various angles to map the surrounding environment using optical techniques. Existing commercial LIDARs offer a long range of measurement, allowing mapping of the surroundings. However, such sensors often have centimeter accuracy and a minimum scan range, resulting in a blind area and are generally unsuitable for compact spaces and areas with high density of neighboring objects. This paper presents a framework for using a customized 2D laser scanner, an IMU (Inertial Measurement Unit) and a data fusion approach for localization inside high-density volumes such as nuclear reactors. The laser scanner offers precise measurements with submillimeter accuracy for items located in the short range. The IMU calculates the robot attitude angles, which are critical for inclination angle corrections. The facilities are often made of metallic materials with highly reflective surfaces, which remains problematic for the laser scanner. A mock-up nuclear dome, of realistic material construction, was utilized to benchmark the performance of this framework. The distance and orientation error observed were below 2 mm and 1°, respectively. The framework will be further processed to produce a close-range environment mapping.

A Guided Wave Inspection Technique for Wedge Features.

Numerous engineering components feature prismatic wedge-like structures that require nondestructive evaluation (NDE) in order to ensure functionality or safety. This article focuses on the inspection of the wedge-like seal fins of a jet engine drum, though the capabilities presented will be generic. It is proposed that antisymmetric flexural edge modes, feature-guided waves localized to the wedge tips, may be used for defect detection. Although analytical solutions exist that characterize the ultrasonic behavior of ideal wedges, in practice, real wedges will be irregular (containing, for example, truncated tips that are built onto an associated structure or have nonstraight edges), and therefore, generic methodologies are required to characterize wave behavior in nonideal wedges. This article uses a semianalytical finite-element (SAFE) methodology to characterize the guided waves in wedge-like features with irregular cross sections to assess their suitability for NDE inspection and compare them with edge modes in ideal wedges. The science and methodologies required in this article are necessary to select an appropriate operating frequency for the particular application at hand. In addition, this article addresses the practical challenge of excitation and detection of flexural edge modes by presenting a piezoelectric-based dry-coupled transducer system suitable for pulse-echo operation. This article, therefore, presents the scientific basis required for industrial exploitation, together with the practical tools that facilitate use. The study concludes with the experimental demonstration of the edge wave-based inspection of a seal fin, achieving a signal-to-noise ratio of 28 dB from a 0.75-mm radial tip defect.

Machining-Based Coverage Path Planning for Automated Structural Inspection

The automation of robotically delivered nondestructive evaluation inspection shares many aims with traditional manufacture machining. This paper presents a new hardware and software system for automated thickness mapping of large-scale areas, with multiple obstacles, by employing computer-aided drawing (CAD)/computer-aided manufacturing (CAM)-inspired path planning to implement control of a novel mobile robotic thickness mapping inspection vehicle. A custom postprocessor provides the necessary translation from CAM numeric code through robotic kinematic control to combine and automate the overall process. The generalized steps to implement this approach for any mobile robotic platform are presented herein and applied, in this instance, to a novel thickness mapping crawler. The inspection capabilities of the system were evaluated on an indoor mock-inspection scenario, within a motion tracking cell, to provide quantitative performance figures for positional accuracy. Multiple thickness defects simulating corrosion features on a steel sample plate were combined with obstacles to be avoided during the inspection. A minimum thickness mapping error of 0.21 mm and a mean path error of 4.41 mm were observed for a 2 m2 carbon steel sample of 10-mm nominal thickness. The potential of this automated approach has benefits in terms of repeatability of area coverage, obstacle avoidance, and reduced path overlap, all of which directly lead to increased task efficiency and reduced inspection time of large structural assets. Note to Practitioners —Current industrial robotic inspection approaches largely consist of a manual control of robotic platform motion to desired points, with the aim of producing a number of straight scans for larger areas, often spaced meters apart. The structures featuring large surface area and multiple obstacles are routinely inspected with such manual approaches, which are both labor intensive and error prone, and do not guarantee acquisition of full area coverage. The presented system addresses these limitations through a combined hardware and software approach. Core to the operation of the system is a fully wireless, differential drive crawler with integrated active ultrasonic wheel probe, to provide remote thickness mapping. Automation of the path generation algorithms is produced using the commercial CAD/CAM software algorithms, and this paper sets out an adaptable methodology for producing a custom postprocessor to convert the exported G-codes to suitable kinematic commands for mobile robotic platforms. The differential drive crawler is used in this paper to demonstrate the process. This approach has benefits in terms of improved industrial standardization and operational repeatability. The inspection capabilities of the system were documented on an indoor mock-inspection scenario, within a motion tracking cell to provide quantitative performance figures for the approach. Future work is required to integrate the on-board positioning strategies, removing the dependence on global systems, for full automated deployment capability.

A Study on Non Destructive Evaluation of the Steam Turbine L-0 Blades

The Nuclear and Fossil Steam Turbines record a considerable number of failures annually. Some of these failures reported are as result of blade failure. The failure of the L-0 blade in a Steam Turbine is one of the most reported blade failure in Nuclear and Fossil steam turbines. This paper seeks to identify the best Non Destructive Evaluation (NDE) method or methods to be used in the steam turbine L-0 blades inspection process. The development of systems engineering processes presents an opportunity to apply NDE inspection to the L-0 blades. This process apply computer modelling of the L-0 using ANSYS and by simulating the stresses experienced by the L-0 blade during operation it is possible to identify the most susceptible areas for crack formation and growth. The results from these models compared to industry data for validation. The analysis of these results used to predict the most probable failure location and failure modes. Therefore NDE inspection can be applied to these areas with greater degree of accuracy. This would be beneficial in the increasing the accuracy in the detection of cracks and hence save inspection time and the overall inspection cost. Furthermore, not only the location for crack formation and NDE inspection determined but also best the NDE inspection technique/techniques to be applied appropriately on the L-0 blade are prescribed.

Improved detection of rough defects for ultrasonic nondestructive evaluation inspections based on finite element modeling of elastic wave scattering.

Defects which possess rough surfaces greatly affect ultrasonic wave scattering behavior, usually reducing the magnitude of reflected signals. Understanding and accurately predicting the influence of roughness on signal amplitudes is crucial, especially in nondestructive evaluation (NDE) for the inspection of safety-critical components. An extension of Kirchhoff theory has formed the basis for many practical applications; however, it is widely recognized that these predictions are pessimistic because of analytical approximations. A numerical full-field modeling approach does not fall victim to such limitations. Here, a finite element (FE) modeling approach is used to develop a realistic methodology for the prediction of expected backscattering from rough defects. The ultrasonic backscatter from multiple rough surfaces defined by the same statistical class is calculated for normal and oblique incidence. Results from FE models are compared with Kirchhoff theory predictions and experimental measurements to establish confidence in the new approach. At lower levels of roughness, excellent agreement is observed between Kirchhoff theory, FE, and experimental data, whereas at higher values, the pessimism of Kirchhoff theory is confirmed. An important distinction is made between the total, coherent, and diffuse signals and it is observed, significantly, that the total signal amplitude is representative of the information obtained during an inspection. This analysis provides a robust basis for a less sensitive, yet safe, threshold for inspection of rough defects.

Uncertainty in Common NDE Techniques for Use in Risk-Based Bridge Inspection Planning: Existing Data

AbstractIn 2009, a joint American Society of Civil Engineers Structural Engineering Institute and American Association of State Highway and Transportation Officials ad hoc group was created to study how current bridge practices could be improved for the future. Among their recommendations was the wider use of nondestructive evaluation (NDE) techniques. Risk-based planning to determine the appropriate inspection frequency, scope, intensity, and methodology would help make the increased time and costs associated with NDE inspections feasible for management budgets. However, a rigorous risk-based approach requires that the accuracy and reliability be quantified for each test. This paper serves as a starting point for quantifying these important variables and identifies the type of information that is still needed. Common NDE techniques were identified, and existing studies were reviewed to identify what data relevant to accuracy and reliability are currently available for these techniques. This review indica...

The probability of detection for structural health monitoring systems: Repeated measures data

The United States Air Force currently relies on schedule-based inspections using nondestructive evaluation methods for ensuring airframe integrity. The sensitivity of a nondestructive evaluation method is quantified statistically using a probability of detection process. The purpose of the probability of detection process is to generate a metric for a given nondestructive evaluation technique and corresponding defect (e.g. crack). This process could be conducted under various inspection conditions and defect sizes. The set of factors varied in the process is controlled to allow each nondestructive evaluation inspection to be treated as statistically independent. Current United States Air Force structural inspections are performed at time intervals that adhere to the independence assumption. However, the United States Air Force plans to service airframes based on their actual condition instead of the current schedule-based approach. Accordingly, there is emphasis on developing advanced health management technologies, such as structural health monitoring systems, which provide an automated and real-time assessment of a structure’s ability to serve its intended purpose. Therefore, structural health monitoring is considered to be equivalent to an in situ nondestructive evaluation structural inspection device. With a structural health monitoring system, the time interval between inspections will be much smaller than the time intervals between nondestructive evaluation inspections. Since structural health monitoring measurements are from the same sensors, in the same location, the independent measurement assumption used to analyze nondestructive evaluation methods is invalid. In this article, we present a statistical method consistent with current probability of detection process, yet designed to appropriately analyze dependent data. We demonstrate this method first with simulated data and then with experimental data from three test specimens of a representative aircraft structural component. This method leverages the advantages of a structural health monitoring system through its frequent measurements while maintaining its usefulness through appropriately computed probability of detection values. Furthermore, we present a numerical method for estimating the number of test specimens needed to achieve a desired value.

Applications of Ultrasonic Techniques in Oil and Gas Pipeline Industries: A Review

The diversity of ultrasound techniques used in oil and gas pipeline plants provides us with a wealth of information on how to exploit this technology when combined with other techniques, in order to improve the quality of analysis. The fundamental theory of ultrasonic nondestructive evaluation (NDE) technology is offered, along with practical limitations as related to two factors (wave types and transducers). The focus is limited to the two main techniques used in pipe plants: First, straight beam evaluation and second, angle beam evaluation. The depth of defect (DD) is calculated using straight beam ultrasonic in six different materials according to their relative longitudinal wave (LW) velocities. The materials and respective velocities of LW are: rolled aluminum (6420 m/s), mild steel (5960 m/s), stainless steel-347 (5790 m/s), rolled copper (5010 m/s), annealed copper (4760 m/s), and brass (4700 m/s). In each material eight defects are modeled; the first represents l00% of the material thickness (D), 50.8 mm. The other seven cases represent the DD, as 87.5% of the material thickness, 75%, 62.5%, 50%, 37.5%, 25%, and 12.5%, respectively. Using angle beam evaluation, several parameters are calculated for six different reflection angles (βR) (45°, 50°, 55°, 60°, 65° and 70°). The surface distance (SD), ½skip distance (SKD), full SKD, and 1½SKD,½sound path (SP) length, full SP, and 1½ SP are calculated for each βR. The relationship of SKD and SP to the βR is graphed. A chief limitation is noted that ultrasound testing is heavily dependent on the expertise of the operator, and because the reading of the outcome is subjective, precision may be hard to achieve. This review also clarifies and discusses the options used in solving the industrial engineering problem, with a comprehensive historical summary of the information available in the literature. Merging various NDE inspection techniques into the testing of objects is discussed. Eventually, it is hoped to find a suitable technique combined with ultrasonic inspection to deliver highly effective remote testing.

Statistical Assessment of Probability of Detection for Automated Eddy Current Nondestructive Evaluation Inspection

Eddy current inspection is widely used in nondestructive evaluation to detect cracks in metal structures. The impedance plane measurement response, collected using our motion controlled eddy current inspection system, is used in the analysis. A scalar reduction from the impedance plane response is used to minimize human-factor variation. We apply a noise interference model to the data from a large-scale experiment taking measurements on fastener rivet holes in multilayer structures with fatigue cracks, and estimate the probability of detection (POD). The estimates of POD as a function of crack size will be valuable for future model-assisted POD studies.

Knowledge-based support in Non-Destructive Testing for health monitoring of aircraft structures

Maintenance manuals include general methods and procedures for industrial maintenance and they contain information about principles of maintenance methods. Particularly, Non-Destructive Testing (NDT) methods are important for the detection of aeronautical defects and they can be used for various kinds of material and in different environments. Conventional non-destructive evaluation inspections are done at periodic maintenance checks. Usually, the list of tools used in a maintenance program is simply located in the introduction of manuals, without any precision as regards to their characteristics, except for a short description of the manufacturer and tasks in which they are employed. Improving the identification concepts of the maintenance tools is needed to manage the set of equipments and establish a system of equivalence: it is necessary to have a consistent maintenance conceptualization, flexible enough to fit all current equipment, but also all those likely to be added/used in the future. Our contribution is related to the formal specification of the system of functional equivalences that can facilitate the maintenance activities with means to determine whether a tool can be substituted for another by observing their key parameters in the identified characteristics. Reasoning mechanisms of conceptual graphs constitute the baseline elements to measure the fit or unfit between an equipment model and a maintenance activity model. Graph operations are used for processing answers to a query and this graph-based approach to the search method is in-line with the logical view of information retrieval. The methodology described supports knowledge formalization and capitalization of experienced NDT practitioners. As a result, it enables the selection of a NDT technique and outlines its capabilities with acceptable alternatives.

A Truncated Logistic Regression Model in Probability of Detection Evaluation

ABSTRACT In nondestructive evaluation (NDE) studies, the probability of detection curve (POD) is an important performance metric. The traditional POD estimation is to conduct NDE inspections for artificially fabricated specimens with known flaws. This approach is often challenges because not only do fabricated flaws not adequately represent the flaws found in the field, but the cost and time of fabricating artificial specimens can also be very high. In practice, field samples and components in service with naturally occurring defects are readily available and much less expensive to test. However, the disadvantage of this field approach is that the exact number and sizes of the flaws in a sample (especially for flaws with small sizes) are unknown. As a result, serious bias in estimating the POD can occur. In this article, a truncated logistic regression method is developed that can estimate POD accurately and consistently with field samples based on multiple inspections. A case study illustrates the successful application of the proposed approach in a leading manufacturing company. The simulation studies also show that the proposed methods estimate the POD on field samples with quality comparable to that of the traditional approach on artificial specimens.

Composite non-destructive evaluation system for probability of detection assessment

In non-destructive evaluation (NDE) studies, the probability of detection (POD) curve is an important performance metric for an NDE system. POD curve estimation and NDE capability assessment are based on the flaws detected and the flaws missed. In order to get the information of undetected flaws, fabricated artificial flaws are often used which provide complete information of defects in most of laboratory demonstrations. However, this practice is too expensive and impractical to use in the field environment. In this study, a combination of several additional NDE inspection systems, called composite NDE system, is proposed to assist the POD evaluation based on field inspection. A simulation model is built to evaluate the effectiveness of the composite NDE system. The simulation result of this approach is verified and compared with traditional POD evaluation methods.

Visual and nondestructive evaluation inspection of live gas mains using the Explorer family of pipe robots

Visual inspection and nondestructive evaluation (NDE) of natural gas distribution mains is an important future maintenance cost-planning step for the nation's gas utilities. These data need to be g...

Visual and nondestructive evaluation inspection of live gas mains using the Explorer™ family of pipe robots

AbstractVisual inspection and nondestructive evaluation (NDE) of natural gas distribution mains is an important future maintenance cost‐planning step for the nation's gas utilities. These data need to be gathered at an affordable cost with the fewest excavations and maximum linear feet inspected for each deployment, with minimal to no disruption in service. Current methods (sniffing, direct assessment) are either postleak reactive or too unreliable to offer a viable and Department of Transportation–acceptable approach as a whole. Toward achieving the above goal, a consortium of federal and commercial sponsors funded the development of Explorer™. Explorer™ is a long‐range, untethered, self‐powered, and wirelessly controlled modular inspection robot for the visual inspection and NDE of 6‐ and 8‐in. natural gas distribution pipelines/mains. The robot is launched into the pipeline under live (pressurized flow) conditions and can negotiate diameter changes, 45‐ and 90‐deg bends and tees, as well as inclined and vertical sections of the piping network. The modular design of the system allows it to be expanded to include additional inspection and/or repair tools. The range of the robot is an order of magnitude higher (thousands of feet) than present state‐of‐the‐art inspection systems and will improve the way gas utilities maintain and manage their systems. Two prototypes, Explorer‐I and ‐II (X‐I and X‐II), were developed and field‐tested over a 3‐year period. X‐I is capable of visual inspection only and was field‐tested in 2004 and 2005. The next‐generation X‐II, capable of visual and NDE inspection [remote field eddy current (RFEC) and magnetic flux leakage (MFL)] was developed thereafter and had field trials in 2006 and late 2007. It was successfully deployed into low‐pressure (<125 psig) and high‐pressure (>500 psig) distribution and transmission natural gas mains, with multi‐1,000‐ft inspection runs under live conditions from a single excavation. This paper will describe the overall engineering design and functionality of the Explorer™ family of robots, as well as the results of the field trials for both platforms. It will highlight the importance of the various design and safety features of the in‐pipe crawler and showcase the value of data types and position‐tagged visual∼NDE data collected in working pipelines under live flow conditions. © 2010 Wiley Periodicals, Inc.