Inspection System Research Articles

Qualification Of Inspection Systems In Nuclear Power Plants. Looking For Excellence

Inspection reliability is an area of growing interest. In particular, in certain industrial sectors involving critical structures, such as nuclear power plants for energy generation, reliability of Non-Destructive Examinations (NDE) has received special attention since more than 40 years. Many initiatives and projects (PISC, EPRI NDE, ENIQ, etc.) developed and generated new findings and results. In addition, the inspection systems are regularly optimized for improving the NDE reliability as much as possible. In the early 1990s, the European Network for Inspection Qualification (ENIQ) continued the PISC NDE reliability research and, few years later, issued the European Methodology for Qualification of Non-Destructive Testing. The methodology considers the NDE system follows the modular model of reliability as well as complementary tasks to address human factors issues. Then, every country develops his own methodology based on the existing legal requirements and the ENIQ Methodology. This is the case of Spain, in which the CSN (the Nuclear Safety Body) requires the qualification of inspection systems before their application in the Nuclear Power Plants (NPP). This paper describes the main characteristics of the Spanish Methodology as well as examples to facilitate the understanding of the ideas behind the approach that currently applied in the Spanish NPPs since the mid-2000s.

Digital twin-enhanced robotic system for remote diesel engine assembly defect inspection

Purpose This study aims to streamline and enhance the assembly defect inspection process in diesel engine production. Traditional manual inspection methods are labor-intensive and time-consuming because of the complex structures of the engines and the noisy workshop environment. This study’s robotic system aims to alleviate these challenges by automating the inspection process and enabling easy remote inspection, thereby freeing workers from heavy fieldwork. Design/methodology/approach This study’s system uses a robotic arm to traverse and capture images of key components of the engine. This study uses anomaly detection algorithms to automatically identify defects in the captured images. Additionally, this system is enhanced by digital twin technology, which provides inspectors with various tools to designate components of interest in the engine and assist in defect checking and annotation. This integration facilitates smooth transitions from manual to automatic inspection within a short period. Findings Through evaluations and user studies conducted over a relatively long period, the authors found that the system accelerates and improves the accuracy of engine inspections. The results indicate that the system significantly enhances the efficiency of production processes for manufacturers. Originality/value The system represents a novel approach to engine inspection, leveraging robotic technology and digital twin enhancements to address the limitations of traditional manual inspection methods. By automating and enhancing the inspection process, the system offers manufacturers the opportunity to improve production efficiency and ensure the quality of diesel engines.

Detecting and evaluating surface defects in additive manufacturing composite materials via image processing technique

Purpose This study aims to offer an image-based robust edge detection system that can estimate, identify, locate and label surface flaws during manufacturing for real-time surface issue diagnostics. Of great concern, this methodology extrapolates surface defect information from scanning electron microscopy (SEM) images of composite fracture surfaces. This study predicts changes in topological intensity of composite fracture surfaces and display them as real-time surface intensity values for the first time. Design/methodology/approach This work, however, introduces a novel robust edge detection method – based image processing – as it is shown to be effective in locating defects, as measured by SEM images of composite fracture surfaces created using additive manufacturing (AM). SEM images, obtained in this study, are related to previous study considering the fracture surfaces of reinforced thermoset composites created via the AM method. These SEM images are of two types: fracture surface of AM of carbon fiber reinforced thermoset composites and fracture surface of AM of syntactic foam reinforced thermoset composites. Initially, MATLAB environment is used for analyzing the SEM images; the technique used, as well as the validity are explained more in the methodology section. Findings The robust surface defect inspection approach used herein is found to be capable of predicting, identifying, localizing and labeling surface defects during production, allowing for real-time surface issue diagnosis. Further, this work makes it possible to use image processing and analysis of these surfaces to anticipate fluctuations in the topological intensity of the fracture surfaces of composites and represent them as values of surface intensity in real time. Originality/value Rising worldwide company rivalry requires a fast, accurate component failure diagnostic method. To create an efficient feature set, a surface defect inspection system must identify product flaws in real time. Thus, this study proposed an image-based robust edge detection system – based on MATLAB environment – that is capable of estimating, identifying, locating and labeling surface faults during production. This paves the way for an extensive set of high-quality tools for dealing with a wide range of problems associated with digital image processing in composites. As a result, the ability to define methodologies and rapidly prototype prospective solutions typically minimizes the cost and time required to implement a successful system during the design phase of an image processing system.

Rail Detection Research Status of Multi -Sensing Technology

Traditional rail inspection methods, primarily relying on manual inspection and basic equipment, face significant challenges including low efficiency, poor accuracy, and susceptibility to human error. This paper presents a comprehensive review of the current research status and future trends in multi-sensor rail transit inspection vehicles. The focus is on the integrated application of various sensing technologies, particularly inertial systems and visual sensing, to achieve high-precision and efficient measurement of rail geometrical parameters. An in-depth analysis of key technologies and challenges in railway track inspection is provided, exploring how advanced sensing methods can overcome the limitations of traditional approaches. The paper also examines the market prospects for rail transit inspection, highlighting the growing demand for more sophisticated and reliable inspection systems. Furthermore, potential future research directions in this field are outlined, including the development of AI-powered data analysis, real-time defect detection algorithms, and the integration of emerging technologies such as LiDAR and thermal imaging. This review aims to provide valuable insights for researchers, engineers, and industry professionals working on advancing rail inspection technologies.

Intelligent Manufacturing in Wine Barrel Production: Deep Learning-Based Wood Stave Classification

Innovative wood inspection technology is crucial in various industries, especially for determining wood quality by counting rings in each stave, a key factor in wine barrel production. (1) Background: Traditionally, human inspectors visually evaluate staves, compensating for natural variations and characteristics like dirt and saw-induced aberrations. These variations pose significant challenges for automatic inspection systems. Several techniques using classical image processing and deep learning have been developed to detect tree-ring boundaries, but they often struggle with woods exhibiting heterogeneity and texture irregularities. (2) Methods: This study proposes a hybrid approach combining classical computer vision techniques for preprocessing with deep learning algorithms for classification, designed for continuous automated processing. To enhance performance and accuracy, we employ a data augmentation strategy using cropping techniques to address intra-class variability in individual staves. (3) Results: Our approach significantly improves accuracy and reliability in classifying wood with irregular textures and heterogeneity. The use of explainable AI and model calibration offers a deeper understanding of the model’s decision-making process, ensuring robustness and transparency, and setting confidence thresholds for outputs. (4) Conclusions: The proposed system enhances the performance of automatic wood inspection technologies, providing a robust solution for industries requiring precise wood quality assessment, particularly in wine barrel production.

Optimal path planning for unmanned aerial vehicles in power line inspection in rechargeable scenario

To address the issue that a single charge of UAVs (Unmanned Aerial Vehicles) do not satisfy task requirements, this paper proposes a solution involving the deployment of charging stations in the mission area. An energy-efficient path planning method is designed for UAVs with charging stations, which simultaneously determines the deployment locations and quantities of charging stations. The method formulates the energy optimization path planning problem of UAVs as an integer linear programming and an algorithm based on greedy strategy is designed for solving the model. A numerical example including simulations across various scenarios demonstrate that the proposed method can search a more energy-efficient paths compared to single-agent multi-sortie schemes with a greedy strategy. This research contributes to the development of intelligent and autonomous UAV systems for power line inspection, enhancing grid reliability and safety.

3D micromorphological reconstruction and roughness characterization of wood surface based on sequence images

A novel non-contact optical inspection technique is proposed to precisely reconstruct the three-dimensional surface structure of wood and evaluate its roughness. The feasibility of using image fusion techniques to characterize the 3D roughness of wood surfaces is explored by combining high-resolution serial image acquisition techniques with advanced data processing methods. The work focuses on examining various cut surfaces of three distinct wood species: Larch, Catalpa, and Toona sinensis. The proposed 3D roughness parameters are verified using 3D reconstruction and precise measurements. The experimental results demonstrate that the proposed method exhibits strong adaptability in measuring wood surface roughness and achieves a high degree of consistency with the results obtained from traditional digital microscope systems. This study not only validates the possible use of multilayer sequence image fusion technology in measuring wood roughness, but also establishes a solid basis for the creation of 3D assessment criteria for wood surface roughness and associated inspection systems.

Development of Wall Hammering Inspection Systems Using Two-Wheeled Multi-Copters

In this study, we investigated inspection robots that can safely, accurately, and quickly perform hammer sound inspections on tile exterior walls and present new wall hammering inspection systems using two-wheeled multi-copters. Our results yielded the following five advantages. First, the proposed multi-copter can use its wheels not only to move freely on the walls of buildings but also to overcome obstacles on the walls. Therefore, almost any tile wall surface can be inspected for hammering sound. Second, the multi-copter was equipped with both hammer-shaped rods to hit the tile exterior wall and microphones to collect the hammer sound of the tile, while moving quickly on the wall surface. Third, the wall hammering inspection systems can be used safely with a work assistance mechanism using a wire even in densely populated areas, such as urban areas, because the multi-copter has high wind resistance against crosswinds by both pushing against the wall and operating by the wire. Fourth, we presented some automatic control systems that make the multi-copter operation easy during hammering inspections and demonstrated its usefulness through experiments. Fifth, we proposed useful methods for detecting floating tiles based on the hammering results, conducted some outdoor flight experiments on building exterior walls, and demonstrated that floating tiles can be determined with high accuracy.

Comparison of Immersion and Portable Ultrasonic Housing to Quantify the Adhesive Bond Thickness and Sizing of Foreign Objects.

High-performance materials, such as carbon fiber laminates, are costly to manufacture and are often used in demanding environments requiring the use of high-resolution non-destructive testing (NDT) methods to confirm the integrity of the parts. One NDT method that has shown promise for qualifying carbon fiber laminates is the use of immersion ultrasound with spherically focused probes. However, many parts may not be submersible in an immersion tank due to size or material constraints. These parts must be scanned with contact transducers with inferior resolutions or with expensive and messy systems such as bubblers. This research presents the use of a novel housing system that allows for the use of focused immersion transducers in an out-of-tank portable ultrasonic scanning application. This work presents a comparison between scans taken using a custom high-resolution immersion system and scans taken using the presented housing. There are a wide variety of potential inspection applications for this novel system, and the present work focused on two specific applications: the quantification of the spatially varying adhesive thickness in bonded carbon fiber laminates and the quantification of foreign object inclusions in carbon fiber laminates. The results presented show that scans using the portable housing are comparable in quality to scans performed using an immersion system. Specifically, both inspection approaches had an average error of 0.04 mm when quantifying the adhesive thickness of a bonded composite, and for the foreign object detection, the error in quantifying the dimensions of the embedded foreign object was 0.1 mm and 0.2 mm for the immersion system and the portable inspection system, respectively. The demonstration was performed in a laboratory setting, but a discussion is provided for the necessary improvements needed to extend the system for use in field applications.

Design and Development of a Wayside AI‐Assisted Vision System for Online Train Wheel Inspection

ABSTRACTWayside inspection of rolling stock has been around for some time and wheel impact load and fiber‐grating sensors are actively explored for getting high‐fidelity data. Visual inspection from wayside provides the opportunity to gain high‐resolution data, which can help in the early diagnosis of potential faults. It is rarely explored due to complexities associated with calibration, moving and rotating targets, and difficulties associated with data acquisition. This paper explores and presents an in‐depth design and development strategy for such systems. It presents the development steps, implementation, and results of a vision inspection system for regular and automated inspection of train wheels. First, various configurations for positioning of the cameras in a three‐dimensional setting are considered and discussed, followed by online data acquisition for establishing a data set. Later, a comprehensive comparative analysis was conducted on several object detection algorithms for wheel segmentation task. Different algorithms are evaluated using COCO evaluation metrics, and the best‐performing model, YOLOv9, achieves a mAP50 of 0.94 and a recall of 0.91. The developed system has produced satisfactory results in acquiring proper wheel tread images and segmenting the wheel. Further avenues for countering lighting issues and defect detection are provided.

Optical Design Study with Uniform Field of View Regardless of Sensor Size for Terahertz System Applications

The focal length in a typical optical system changes with the angle of view, according to the size of the sensor. This study proposed an optical terahertz (THz) system application where the focal length changed while the angle of view was fixed; thus, the image height was variable and responded to various sensor sizes. Therefore, it is possible to respond to various sensors with one optical system when the inspection distance is fixed. The fundamental optical system was designed by arranging the refractive power, which was determined according to the sensor size using the Gaussian bracketing method. A zoom optical system that changed the image height by fixing the angle of view and changed the focal length by moving the internal lens group was designed. THz waves exhibit minimal change in the refractive index depending on the wavelength. Moreover, their long-wavelength characteristics facilitate the development of millimeter-level pixel sizes. Therefore, the root mean square size of the maximum spot was 0.329 mm, which corrected the aberration to less than 1 mm (smaller than the pixel size). Further, a lighting analysis at 3 and 6 m locations confirmed the expansion of the lighting area by the magnification of the sensor size. After turning off certain light sources, we checked the contrast ratio via lighting analysis and confirmed that the size of one pixel was clearly distinguishable. Consequently, this newly designed optical system performed appropriately as an optical inspection system for THz system applications.

Experimental Evaluation of the Possibility of Through Defects Detection in Thermally Expanded Graphite Workpieces by Acoustic Method

The paper presents the results of evaluating the possibility of detecting defects (e.g., through holes) in workpieces made of thermally expanded graphite used as sealing materials using a non-contact air-coupled acoustic inspection system. The deviation of the acoustic signal amplitude when it passes through a sample of thermally expanded graphite in defective and defect-free regions is used as an informative parameter. The obtained dependences of the change in the amplitude of the passed signal through the sample on the size of the artificial through hole, density and thickness of the object showed a significant influence of these parameters on the detectability of the defect, which should be taken into account in the development of inspection technologies and in the process of its implementation. The change in the amplitude of the signal passed through the object directly depends on the increase in the parameters of the sample (density, thickness) and the size of the through hole, which increases the sensitivity of acoustic control and detectability of defects.On the basis of the obtained dependences the criteria for rejection of workpieces can be developed at the stage of production or immediately before manufacturing of their products. The influence of defect positioning relative to the acoustic axis of the transmitter-receiver system is shown, which leads to a decrease in the informative parameter, and consequently to a decrease in sensitivity. This dependence is characteristic for all thicknesses and densities of objects, as well as for all sizes of artificial through holes when control is carried out immediately after defect formation. When acoustic inspection is carried out some time after the defect formation, the maximum of this dependence may shift due to the shift of the amount of substance to fill the defect cavity, which is confirmed by microscopy. The obtained results can be used in the development of workpieces acoustic control parameters.

Quality inspection method of BIM model for power grid engineering based on knowledge graph

With the continuous updating of national specifications and industry standards, the traditional manual compliance review method is difficult to meet the needs of the development of the construction industry. This study aims to improve the quality review method of BIM models by using knowledge graph technology. The BIM model of power grid engineering is selected as the research object, and a BIM model inspection system based on knowledge graph is developed to verify whether it complies with the relevant specifications and standards of power grid engineering. By taking the BIM model of an electrical engineering project as a case, an inspection report was generated, and the results verified the feasibility and practicality of this method. This technology provides a new direction for the deep integration of architectural design and information technology.

Implementation strategy for launch and performance improvement of high throughput manufacturing inspection systems

Product technologies are changing rapidly in advanced automotive propulsion systems. These products are driving the need for new manufacturing processes and new inspection methods. To keep new propulsion systems affordable and ensure these new products are introduced with high quality, automotive manufacturers are seeking automated inspection solutions with low cost and near-zero error rates to inspect 100% of the items. In this paper, a progressive deployment strategy of a hybrid inspection system is presented and studied in the context of technology development and rapid deployment. It enabled us to begin with human inspection and gradually phase-in automated inspection technology, while almost never failing to identify a bad item. This strategy was applied successfully to inspect ultrasonic welds in lithium ion battery packs. At the time of this study, a 75% reduction in human inspection was achieved with prospects for further reduction. Actual results from the implementation of this strategy in production are presented. Recommendations are made regarding the most appropriate time to employ this strategy and how it could increase the use of advanced automated in-line inspection technologies.

Optical inspection of stator slots for electric motors

An optical non-contact inspection system was developed for measuring the slots in stator lamination stacks. To avoid passing go/no-go gage blocks through the slots, a machine vision system is instead used to measure the stator core slots and identify the presence of burrs within the slots. Utilizing telecentric optics along with an alignment monitoring system configured to monitor and orient the stator core, the core slots can be oriented relative to the imaging axis for further metrology measurements. Among these measurements, the smallest opening dimensions (slot width and depth) of each slot due to misalignment of laminations and the detection of burrs along the edges of the slots throughout the length of the lamination stack are critical for full stator assembly. Advanced image processing algorithms were developed to obtain sub-pixel accuracy which is required to measure the slots. This, used in conjunction with a robust vision calibration technique, increases the feasibility of building a device that can be implemented as a production inspection system. Experiments show the reliability of the computer vision approach and how it can be used in the inspection of slots in lamination stacks.

Safety hazard inspection system during typhoon warning period to improve the disaster resistance ability of road and bridge construction site

Typhoons are frequent in coastal cities, and the vulnerability of road and bridge construction sites is high, but existing studies have not focused on the safety management of road and bridge construction sites under typhoon disasters. Traditional manual safety inspections suffer from problems such as low efficiency, high subjectivity, and incomplete coverage. Therefore, this study aims to develop an intelligent safety management system for road and bridge construction sites during the typhoon warning period, in order to achieve rapid and automated typhoon prevention and control. In this study, the automatic identification algorithm of potential safety hazards was developed based on the MATLAB platform, and then the system design of the MATLAB GUI was carried out, which ultimately forms the automatic safety hazard inspection and measure enquiry system during the typhoon warning period for road and bridge construction sites (RBS-TY). The system can realize the detection of vertical deformation, safety distance and safety height by using the point cloud data of the object to be measured. According to the identified safety hazards, it will automatically retrieve the database of typhoon safety measures in the road and bridge construction site and provide the countermeasures in the GUI interface. Experimentally verified results show that this system can assist safety management personnel to quickly guide typhoon prevention and control work during the 24-h typhoon warning period, improve the efficiency of safety management, and help to promote the development of intelligent typhoon emergency response technology at road and bridge construction sites.

Research of Converter Station Intelligent Inspection System Algorithm

Abstract This article mainly reveals a converter station equipment defect detection system based on the YOLOv5 algorithm. The research encompasses the establishment of a simulated environment for converter station inspection tasks, problem formulation, algorithm design, training process, and experimental evaluation. Key contributions include the enhancement of the YOLOv5 algorithm through improved clustering techniques, dataset preparation, and multi-object detection model training. This highlights the importance of appropriate anchor box initialization in object detection tasks, especially when dealing with small-sized images and varied defect types. In order to verify the correctness of this research, a comparative simulation analysis with mainstream detection algorithms further validates the superiority of the proposed method. The findings of this research contribute valuable insights into the development of AI-driven defect detection systems for power equipment inspection.

Development of a Semi-Autonomous Pulse and Receive Concrete Inspection System

Concrete structures are being subjected to increasing loads and used beyond their intended lifespan. When combined with operators shrinking maintenance budgets it is imperative structures are monitored for damage. Acoustic Emission (AE) and AE Tomography offer a method for damage detection and characterisation. However, they are encumbered by their equipment and setup requirements, and best used on areas where damage is already known to have occurred. An autonomous pulse catch system could be used to identify these areas. To explore the potential of such a system, seven concrete beams differentiated by their aggregate size were tested using an automated pulse and receive system. The effect of aggregate size, and pulse frequency on attenuation and wavespeed are investigated. The pulse regime consisted of narrowband pulses ranging from 30 kHz to 500 kHz. The results show that high frequency pulses experienced greater attenuation than low frequency pulses. Whilst a link appears between the size of the aggregate and the rate of attenuation of high frequency signals. A strong negative correlation between amplitude loss and pulse frequency was observed. For the specimen containing coarse aggregate, pulse velocity was constant between 100 kHz and 500 kHz, whereas specimens containing coarse aggregate experienced frequency dependant attenuation. This testing shows the significant potential for an automated pulse and receive system, which could lead to the development of an autonomous health monitoring system. In addition, the approach can be instrumental in developing large data sets, that can be used in Machine Learning processes, to develop a deeper understanding of AE signal propagation in concrete materials.

The development and status of self-evaluation as a component of school inspection and quality assurance: A comparison across four countries

Objective and rationaleThe increasing prominence of school self-evaluation (SSE) as a pivotal element in global quality assurance practices is attributed to its cost-effectiveness, relevance to school contexts, empowerment of schools in managing and leading the school self-evaluation processes and holding them accountable for achievement standards. This paper aims to explore various approaches and SSE practices through the evolution and current status of school self-evaluation within the realms of school inspection and quality assurance in Dubai, Ireland, New Zealand and Pakistan. The paper emphasises a comparative perspective to showcase SSE models from these countries as pioneering initiatives for quality assurance in educational improvement, especially for nations where SSE practices are still evolving. MethodsThis study employs literature review, document analysis, and semi-structured interviews with school leaders (n = 32) to offer insights into current school self-evaluation practices in these four countries through a comparative analysis. These methods were chosen in line with the specific aims and research questions of the paper which are to provide an overview from the research literature of the development and current status of SSE as an element of school evaluation in each country studied. To analyse, compare and contrast policy in the area in each case and to obtain, through interviews, rich deep data and findings on the attitude and opinions of key personnel in each country on SSE as a policy and practice. ResultsSchool self-evaluation (of varying types) is being built into each school evaluation system and becoming an integral part of inspection and quality assurance, and it is argued here, broadly moving along a similar spectrum in each case. However, for now, SSE practices are different in all four countries, usually due to the stage of development of the school inspection system and, above all, the overall structure of their respective educational landscapes— that is to say, whether it is a centralised, controlled and hierarchical education system or a decentralised and autonomous system. ConclusionThe study suggests that it is possible to integrate school self-evaluation into a wide range of educational systems and philosophies, eventually leading to educational improvement in any country following the case study countries discussed as models.

Delving Into Cluttered Prohibited Item Detection for Security Inspection System

Delving Into Cluttered Prohibited Item Detection for Security Inspection System