Internal Inspection Research Articles

ULTRASONIC INTERROGATION TECHNIQUE FOR DETECTING HATCH ANGLE VARIATIONS IN ADDITIVE MANUFACTURING BY PHONONIC LATTICE STRUCTURES

Abstract Additive Manufacturing/3D Printing (AM/3DP) has revolutionized part production by enabling the creation of intricate internal structures and complex geometries from diverse materials directly from digital design files. Among powder-based metal AM/3DP methods, Selective Laser Melting (SLM) is widely used in advanced applications such as biomedical devices and aerospace parts. Despite considerable progress in AM/3DP and SLM, at present, challenges in print quality persist, and vast resources for post-production quality assessment are allocated. The quality of SLM prints is influenced by various process and design parameters, such as the accuracy of hatch angle deposition, laser intensity/power, scanning speed of the laser beam, print line spacing, layer depth, printing chamber conditions, and the material's physical and chemical properties. Direct ultrasonic Non-Destructive Evaluation (NDE) offers comprehensive internal inspection and real-time data acquisition ability; however, in AM/3DP it faces severe limitations due to a build's intricate internal and external geometric features. In the current study, we present a Phononic Crystal Artifact (PCA)-based real-time ultrasonic NDE quality monitoring framework and show offline its utility in detecting and evaluating hatch angle variations, a critical process parameter. A PCA is substantially simpler and smaller than the actual build but represents its critical geometric and structural intricacies and mechanical properties. The current offline study demonstrates that hatch angle variations can be monitored from ultrasonic responses' spectral modal frequency peaks and wave dispersion relations.

Internal inspection method for crack defects in ferromagnetic pipelines under remanent magnetization

Magnetic flux leakage (MFL) testing is effective in detecting corrosion in ferromagnetic pipelines. However, the MFL generated at the crack is highly weak and is easily drowned in the strong background magnetic field and difficult to detect. Therefore, this paper creatively proposes a remanent magnetic flux leakage (RMFL) detection method. This method utilizes the hysteresis state of the pipeline after MFL detection and identifies crack defects by collecting the remanent magnetic flux density near the inner wall surface of the pipeline. The remanent magnetic field distribution near the pipeline surface is examined at different crack sizes, magnetization speeds, and lift-off values via theoretical analysis, finite element simulation, and experimental verification. In addition, experiments have demonstrated that in terms of crack detection capability, RMFL is superior to MFL. Therefore, adding a RMFL measurement module to the conventional MFL detector has important practical value. It can realize the targeted collaborative application of these two technologies, namely the synchronous detection of corrosion and crack defects, which greatly improves the detection coverage and detection efficiency.

Automated matching and visualisation of magnetic flux leakage data in shale gas pipeline based on ICP and DBSCAN algorithm

ABSTRACT Magnetic flux leakage (MFL) is a non-destructive testing method for shale gas pipeline safety monitoring. Despite many pipelines have completed two or more rounds of internal inspection and have accumulated substantial data, effectively analyzing and utilizing this MFL data remains a significant challenge. To that end, this study proposes a novel combined model that integrates Iterative Closest Point (ICP) and DBSCAN (Density-Based Spatial Clustering of Applications with Noise) fusion to match multiple MFL data. The model outputs the matching result and visualisation images of MFL data acquired in different years. Furthermore, RMSE (root mean square error) and overlap rate have been used to evaluate the model. The results indicate that the average distance error of matched defects between two datasets decreased by 72.5%, and the overlap rate increased by 20%. Additionally, the DBSCAN Fusion shows better computational efficiency with an increase in defect quantity within the pipe segments. Finally, the impact of different datasets on the model’s matching accuracy is discussed in this study. The findings show that small-scale datasets or higher false detection rates lead to decreased accuracy. The proposed model fully leverages MFL data to achieve rapid matching of defects, offering a dependable and effective technical solution for safety monitoring and corrosion prediction in shale gas pipelines.

A Novel Method for Online Diagnostic Analysis of Partial Discharge in Instrument Transformers and Surge Arresters from the Correlation of HFCT and IEC Methods

In this work, a new methodology is proposed for the online and non-invasive extraction of partial discharge (PD) pulses from raw measurement data obtained using a simplified setup. This method enables the creation of sub-windows with optimized size, each containing a single candidate PD pulse. The proposed approach integrates mathematical morphological filtering (MMF) with kurtosis, a first-order Savitzky-Golay smoothing filter, the Otsu method for thresholding, and a specific technique to associate each sub-window with the phase angle of the applied voltage waveform, enabling the construction of phase-resolved PD (PRPD) patterns. The methodology was validated against a commercial PD detection device adhering to the IEC (International Electrotechnical Commission) standard. Experimental results demonstrated that the proposed method, utilizing an off-the-shelf 8-bit resolution data acquisition system and a low-cost high-frequency current transformer (HFCT) sensor, effectively diagnoses and characterizes PD activity in high-voltage equipment, such as surge arresters and instrument transformers, even in noisy environments. It was able to characterize PD activity using only a few cycles of the applied voltage waveform and identify low amplitude PD pulses with low signal-to-noise ratio signals. Other contribution of this work is the diagnosis and fault signature obtained from a real surge arrester (SA) with a nominal voltage of 192 kV, corroborated by destructive disassembly and internal inspection of the tested equipment. This work provides a cost-effective and accurate tool for real-time PD monitoring, which can be embedded in hardware for continuous evaluation of electrical equipment integrity.

Increasing trainee engagement and laboratory feedback during an internal laboratory inspection.

Increasing trainee engagement and laboratory feedback during an internal laboratory inspection.

Internal Quality Inspection and Characterization of Casting Billets Based on Ultrasonic Microscopy

Abstract For evaluating the internal quality of casting billets, the low-power experimental and metallographic methods have the disadvantages of the complex process, the small detection range and the destructive evaluation. In addition, the internal defects of the casting billet are analyzed by the one-dimensional waveform and two-dimensional image for the traditional ultrasonic testing, which can not describe directly the three-dimensional spatial information. Hence, a new method is proposed based on ultrasonic microscopy to detect and characterize the internal defection of the casting billet. In this paper, 42CrMo casting billet is regarded as objective, five sets of samples are fabricated by adjusting temperature gradient field and the number of presses. The ultrasonic sequence images of the present samples are achieved by using ultrasonic microscopy, and the spatial distributions, number, and sizes are obtained for the defects of the casting billet by reconstructing the three-dimensional appearance of microdefects to characterize the internal mass of the casting billet. Finally, the effective and accuracy of the proposed method are validated by using metallographic method.

X-ray radiography and deep learning for internal quality inspection of pear fruit

X-ray radiography and deep learning for internal quality inspection of pear fruit

The effectiveness of food safety self-checking systems in institutional catering

Internal and external food safety audits and inspections are commonly implemented in food service establishments to uphold and enhance food safety standards. While the processes for various types of inspections are similar, each has distinct roles and implications. Self-checking involves inspections conducted by local staff within the food business. However, experts question the efficacy of this process, as it often fails to uncover food safety issues.This indicative study aimed to investigate whether self-checking can reveal food safety risks associated with primary and additional activities and identify which activities, if any, are misjudged by internal staff. An in-depth survey was conducted over four months in 10 school kitchens using a food safety control checklist, which was completed monthly by internal staff, an internal auditor, and an external auditor.The evaluations from external auditors consistently rated the kitchens lower than those from internal auditors and local catering managers. The most divergent judgments centered on personal hygiene practices, storage procedures, cleaning methods, and disinfection processes.Based on this data, food handlers tend to perceive their adherence to food safety practices as more robust than it is.

In-field grading and sorting technology of apples: A state-of-the-art review

Apple is one of the most popular fruits. In-field grading and sorting of apples would enhance growers’ economic benefits by lowering production costs. This study reviews the key components and progress of the quality inspection algorithm for in-field grading and sorting of apples. Four key components (e.g., conveyor, imaging chamber, sorting actuator, and bin filler) are presented in detail, followed by summarizing the shortcomings of these components. The apple’s external (color, size, and defects) and internal quality inspection technologies, such as optical technologies of visible light, near-infrared (NIR), hyperspectral/multispectral imaging (HSI/MSI), and structured illumination (SI) were presented. Despite the excellent detection performance of emerging technologies (e.g., HSI, MSI, and SI), visible light is still dominantly used for in-filed grading. Challenges in getting information on the whole surface area of the apple, uneven lighting, machine size, throughput, and associated costs hamper the commercialization of apple in-field grading and sorting equipment. At present, more efforts should be devoted to internal quality inspection, by developing reliable, fast, and accurate detection equipment and algorithms. With the advancement of sensors and automation algorithms, as well as the emergence of mechanical systems that are suitable for in-field use, it is anticipated that the apple in-field grading and sorting equipment that inspects both external and internal quality will be realized and commercialized in the near future.

Investigations on corrosion rate, mechanical properties and structural homogeneity of interpulse TIG dissimilar weldments

This manuscript aims to investigate the welding characteristics, bead profile, structural integrity and corrosion behavior of low-carbon steel and Monel grade 400 dissimilar joints. The structures were developed using advanced welding technique, gas tungsten constricted arc welding, with Mo-based filler wire. Dissimilar metals after welding have been tested for internal inspection using X-ray radiography and confirmed to be free from all welding defects. Corrosion tests were carried out at various zones of weldment and characterized in a 3.5 wt% NaCl solution at 10 mV/s scan rate. The welding characteristics have been studied by conducting various tests including Vickers hardness, tensile and impact test. To reveal the structural integrity and perform chemical composition analysis of constricted arc weldment, OM/SEM techniques were used. Corrosion rate at steel interface is lower than that at the Monel 400 interface by 9.7% due to the less accumulation of heat. Uniform grain distribution and structural homogeneity were also observed along the weld zone due to constricted arc and controlled heat input.

Intelligent identification method for pipeline ultrasonic internal inspection

ABSTRACT In pipeline ultrasonic testing, the accurate identification of defect types is a prerequisite for quantifying defect sizes. To this end, this paper proposes a defect identification algorithm based on the combination of Wavelet Packet Transform (WPT), Distance Evaluation Technique (DET) and GA-SVM to precisely identify defect boundaries. First, the ultrasonic echo signals are processed using WPT to more accurately extract defect information. Then, DET is employed to eliminate redundant feature parameters, which are sorted by sensitivity to determine the input feature parameters for the GA-SVM. On this basis, a self-developed ultrasonic testing experimental system is used to collect ultrasonic echo signals from Q235 steel pipes with cylindrical, spherical, and conical defects at different spatial orientations of the probe and the pipe. After processing and identifying the signals, it was found that the proposed algorithm achieves an average identification accuracy of 99% for different types of defects, thereby realising automatic identification of pipeline defects. The results provide new ideas and methods for the intelligent identification of volumetric defect types in pipeline ultrasonic internal testing and promote the development of industrial-grade ultrasonic internal detectors.

Diagnostic study on major honeybee disease, pests and predators in North Western Ethiopia.

The study was conducted in Pawe district from Benishangul-Gumuz and Jawi and Fagita Lekoma districts from the Amhara region to investigate major honeybee pests, predators and diseases. Using a purposive sampling technique, 183 households were interviewed, and 240 samples were collected for laboratory analysis of bee disease; data were analysed using descriptive statistics. The share of hive types owned by sampled respondents was 88.6%; overall, 1.1% and 10.3% were traditional, transitional and modern beehives, respectively. About 92% of the sample respondents acquired their base colonies by catching swarm bees on the apex of trees. The majority of beekeepers executed external inspections of their colony, whereas only 50% carried out internal inspections. Based on the responses of beekeepers, around 48.9%, 56.3% and 23.1% of colonies absconded every year from Pawe, Jawi and Fagita Lekoma districts, respectively. Ants, wax moths, bee lice, beetles, spiders, birds, monkeys and honey badgers were the major honeybee pests and predators discovered in study areas in decreasing order. Concerning the incidence of Varroa mites, Nosema apis and amoeba disease, 27.5%, 60% and 71.6% of samples showed positive results in study locations, respectively. From this result, we observed that ants, wax moths, bee lice, beetles, spiders, birds, monkeys and honey badgers were the major honeybee pests and predators. The prevalence of amoeba disease was comparatively higher in highland areas and in the summer season. This finding suggests the need for the alertness of beekeepers in controlling bee disease and pests and strengthening bee colonies through seasonal colony management. There should be a strict quarantine, and check-up undertaken when a new colony is purchased from one region to another is essential.

Spatial Localization of Transformer Inspection Robot Based on Adaptive Denoising and SCOT-β Generalized Cross-Correlation.

In the detection process of the internal defects of large oil-immersed transformers, due to the huge size of large transformers and metal-enclosed structures, the positional localization of miniature inspection robots inside the transformer faces great difficulties. To address this problem, this paper proposes a three-dimensional positional localization method based on adaptive denoising and the SCOT weighting function with the addition of the exponent β (SCOT-β) generalized cross-correlation for L-type ultrasonic arrays of transformer internal inspection robots. Aiming at the strong noise interference in the field, the original signal is decomposed by an improved Empirical Mode Decomposition (EMD) method, and the optimal center frequency and bandwidth of each mode are adaptively searched. By extracting the modes in the frequency band of the positional localization signal, suppressing the modes in the noise frequency band, and reconstructing the Intrinsic Mode Function (IMF) of the independently selected superior modal components, a signal with a high signal-to-noise ratio is obtained. In addition, for the traditional mutual correlation algorithm with a large delay estimation error at a low signal-to-noise ratio, this paper adopts an improved generalized joint weighting function, SCOT-β, which improves the anti-jamming ability of the generalized mutual correlation method at a low signal-to-noise ratio by adding an exponential function to the denominator term of the SCOT weighting function's generalized cross-correlation. Finally, the accurate positional localization of the transformer internal inspection robot is realized based on the quadratic L-array and search-based maximum likelihood estimation method. Simulation and experimental results show the following: the improved EMD denoising method better improves the signal-to-noise ratio of the positional localization signal with a lower distortion rate; in the transformer test tank, which is 120 cm in length, 100 cm in width, and 100 cm in height, based on the positional localization method in this paper, the average relative positional localization error of the transformer internal inspection robot in three-dimensional space is 2.27%, and the maximum positional localization error is less than 2 cm, which meets the requirements of engineering positional localization.

Instrumentation of Cross Electromagnetic Eddy Current Sensing for Internal Pipeline Inspection

Instrumentation of Cross Electromagnetic Eddy Current Sensing for Internal Pipeline Inspection

Additive manufacturing of copper parts using extrusion and sinter-based technology: evaluation of the influence of printing parameters and debinding method

PurposeThis work is focused on the realization of copper parts using the material extrusion additive manufacturing debinding and sintering (MEX+D&S) technology.Design/methodology/approachA highly filled filament with 90 Wt.% of copper is used to realize nine different combinations varying the printing speed and the flow rate. The following thermal debinding and sintering are performed at 483 °C and 1057 °C, respectively, burying the samples in specific refractory powder and carbon. The green and sintered density are measured and an inspection at optical microscope is implemented for a detailed internal analysis of the defects.FindingsThe samples, that reported the highest values of the green density, become the worst in the sintered condition due to evident swelling defect generated by the entrapped polymer during the thermal debinding. On the other hand, the parts with the lower values of green density allowed to achieve a satisfying density value without significant external defects.Originality/valueThe realization of copper parts through laser-based additive manufacturing technologies shows several troubles related to the rapid heat transfer and the high reflectivity of copper, which is a hinder of the absorption of the laser power. The MEX+D&S becomes an easier and economical alternative for the realization of copper parts. The internal inspection of the samples revealed the need for the improvement on the process chain, adopting a different debinding process to open channels during the thermal debinding to avoid the entrapment of the polymer.

Configuration reconstruction and all-joint synchronous measurement based on vision for segmented linkage manipulator of rigid-flexible dual-arm space robot

A rigid-flexible dual-arm space robot offers promising potential for on-orbit operation due to complementary strengths of its rigid and flexible manipulators. The rigid manipulator has the advantage of large payload capacity and high motion accuracy. The segmented linkage flexible manipulator is ideal for maneuvering in narrow, unstructured environments like internal satellite inspections and maintenance, due to its flexibility and slender body. However, there are inevitable tracking errors due to the multiple cable-driven mechanisms in the flexible manipulator. It’s difficult to get the configuration and end pose of flexible manipulator without adding external sensors. To address these issues, this paper presents a method for configuration reconstruction and all-joint synchronous measurement of the flexible manipulator. The flexible manipulator is captured by the stereovision system mounted on the rigid manipulator. The links’ equivalent center points are recognized by central moment method and edge line extraction method based on the natural characteristics. Joint-to-link kinematic model of flexible manipulator is established to measure joint angles and reconstruct configuration. Several simulations and experiments are carried out to validate the proposed method. The experimental results showed links’ average measurement position errors were less than 13 mm and joint angles reconstructed using the proposed method had an error of approximately 0.35°. These results demonstrate the accuracy and robustness of the proposed method.

Identification method of circumferential declination based on amplitude reduction of pipeline ultrasonic internal inspection signals

ABSTRACT The echo signal of ultrasonic internal inspection is highly sensitive to the incident deflection angle between the probe and the measured surface of the pipeline, which is the key factor affecting the accuracy of ultrasonic inspection. In practical engineering, the echo signal of ultrasonic inspection is easy to be interfered by the external environment, which makes it difficult to identify the deflection angle. The data of defect echo signal under different circumferential declination were collected by the pipeline ultrasonic internal inspection system, and the characteristics of signal variation in time domain under different circumferential declination were analysed. The results show that the existence of circumferential declination reduced the absolute value of the overall amplitude of ultrasonic echo signal. A signal processing method based on amplitude reduction is proposed to reduce the sampling random error. Comparative analysis shows that the circumferential declination can be identified based on amplitude reduction method, and the accuracy of ultrasonic recognition can be improved. By extracting the characteristic value of waveform, the empirical formula is obtained. Within 2°, the maximum deviation is 0.137°. The research results have certain guiding significance for the identification of circumferential declination in practical engineering.

Transformer Discharge Carbon-Trace Detection Based on Improved MSRCR Image-Enhancement Algorithm and YOLOv8 Model.

It is difficult to visually detect internal defects in a large transformer with a metal closure. For convenient internal inspection, a micro-robot was adopted, and an inspection method based on an image-enhancement algorithm and an improved deep-learning network was proposed in this paper. Considering the dim environment inside the transformer and the problems of irregular imaging distance and fluctuating supplementary light conditions during image acquisition with the internal-inspection robot, an improved MSRCR algorithm for image enhancement was proposed. It could analyze the local contrast of the image and enhance the details on multiple scales. At the same time, a white-balance algorithm was introduced to enhance the contrast and brightness and solve the problems of overexposure and color distortion. To improve the target recognition performance of complex carbon-trace defects, the SimAM mechanism was incorporated into the Backbone network of the YOLOv8 model to enhance the extraction of carbon-trace features. Meanwhile, the DyHead dynamic detection Head framework was constructed at the output of the YOLOv8 model to improve the perception of local carbon traces with different sizes. To improve the defect target recognition speed of the transformer-inspection robot, a pruning operation was carried out on the YOLOv8 model to remove redundant parameters, realize model lightness, and improve detection efficiency. To verify the effectiveness of the improved algorithm, the detection model was trained and validated with the carbon-trace dataset. The results showed that the MSH-YOLOv8 algorithm achieved an accuracy of 91.80%, which was 3.4 percentage points higher compared to the original YOLOv8 algorithm, and had a significant advantage over other mainstream target-detection algorithms. Meanwhile, the FPS of the proposed algorithm was up to 99.2, indicating that the model computation and model complexity were successfully reduced, which meets the requirements for engineering applications of the transformer internal-inspection robot.

Investigation into flow-induced internal corrosion direct assessment in small-diameter dry gas fluctuating pipeline

Small-diameter undulating natural gas pipelines with low gas transmission capacity do not have internal inspection conditions to grasp the internal corrosion status of the pipeline. This study focuses on a small-diameter undulating dry natural gas gathering and transportation pipeline, the internal corrosion mechanism of the pipeline is studied through corrosion products, and the specific location of the internal corrosion sensitive pipe segment is determined based on multiphase flow simulation and PMDT(pipeline non-contact magnetic anomaly detection); the excavation inspection is ultimately used to evaluate the internal status of the pipeline. The results demonstrate that the internal corrosion mechanism of ZL (ZI Lai) pipeline is O2 and CO2 synergistic corrosion in which O2 plays a dominant role, while the increasing flow rate will aggravate corrosion; The maximum general corrosion rate and critical inclination along the pipeline are 0.0218 mm/a and 18.4°, respectively. Furthermore, twelve corrosion sensitive segments have been identified, including two II-level and six III-level magnetic anomaly segments. The internal corrosion defects are localized at the 3–9o’clock position of the pipeline, with a maximum general corrosion rate not exceeding 0.0228 mm/a. Furthermore, the pipeline failure pressure of 12.27 MPa surpasses the maximum allowable working pressure of 8.84 MPa, ensuring its continued operational safety. The corrosion protection measures of reducing the water content of natural gas, adding vapor corrosion inhibitor and regularly detecting magnetic anomaly pipeline are developed.

A comprehensive evaluation and advancements in dissolved gas analysis for biodegradable oil in photovoltaic solar plants

Over the past three decades, the energy industry has embraced the adoption of a new type of biodegradable oil. Presently, the prevailing international standard for its evaluation is IEEE C57.155–2014. Various working groups, including those within CIGRE A2.68, IEC, and industry manufacturers, have embarked on endeavors to explore new evidence and methodologies for assessing the accuracy and alternatives in detecting failures. These failures are often indicated by elevated levels of seven gases: Hydrogen, Ethane, Acetylene, Methane, Ethylene, Carbon Monoxide, and Carbon Dioxide. Consequently, instances of high oil temperatures have been observed during the optimization of dimensions near hot-spot limits, particularly in renewable energy applications. This research article presents an in-depth analysis of a substantial database comprising 2834 units, aimed at establishing new thresholds for dissolved gas analysis applied to biodegradable oil in photovoltaic solar plants. The developed methodology successfully identified 844 oil samples in poor condition, 332 samples exhibiting one or more failure modes without fault or disconnection, and 1658 units in good condition. Validation of the instrumentation was conducted on 87 transformers through internal inspection, verifying the classification according to the new methodology for evaluating relevant variables and limits of gases through Gaussian distribution and seven algorithms. This validation aimed to enable early detection of failures and establish a health index, achieving an accuracy rate of 90.8 % with support vector machine, 97.6 % with random forest, and 98.3 % with XGBoost. Future research directions should consider evaluating viscosity as a factor in assessing the quality and degradation of transformers utilizing biodegradable oil.