Inspection Device Research Articles

A chemically enriched, repeatedly deformable, and self-recoverable broadband wireless imager sheet

Carbon nanotube (CNT) film-based photo-thermoelectric (PTE) imagers perform functional electromagnetic-wave monitoring, potentially facilitating multimodal non-destructive inspection device usage. The CNT film compositions govern the fundamental device performance, and satisfying high PTE conversion efficiency (higher response and lower noise) is essential for sensitive operations. Although typical sensitive design focuses on minimizing noise, the associated leveling-off response intensity (up to a few millivolts) induces technical limitations in device operations. These issues include mismatching for coupling with compact wireless circuits, being indispensable for on-site inspection applications, and require high-intensity responses at least a few millivolt orders. This work develops chemically enriched PTE imagers comprising semiconducting-separated CNT (semi-CNT) films. While semi-CNTs provide greater intensity thermoelectric responses than semi-metal mixture compositions in the conventional PTE device, the presented imager employs p-/n-type chemical carrier doping to relax inherent significant bottlenecking noise. Such doping enhances material properties for PTE conversion with semi-CNTs up to 4060 times. The imager satisfies similar performances to conventional CNT film devices, including ultrabroadband sensitive photo-detection (with minimum noise sensitivity of 5 pWHz−1/2) under repeatedly deformable configurations, and advantageously exhibits response signal intensity exceeding a few–tens of millivolts. These features enable remote on-site non-destructive PTE imaging inspection with palm-sized wireless circuits.

A portable rapeseed quality non-destructive inspection device based on multichannel spectroscopy

A portable rapeseed quality non-destructive inspection device based on multichannel spectroscopy

Indigenously Developed Robotic Devices for Inspection of Critical Components in Power Plants

The growing global population, combined with rapid industrialization and rising living standards, has resulted in a substantial increase in energy demand. This trend necessitates the development of innovative energy technologies that align with environmental protection policies. As a result, many newly constructed power plants are increasingly utilizing renewable energy sources or cogeneration methods. This dynamic energy landscape has introduced new challenges, as numerous coal plants have transitioned to flexible operations to accommodate the fluctuating availability of renewable energy. To ensure a reliable and uninterrupted electricity supply, power generation utilities must prioritize the reliability of their equipment and systems. The incorporation of robotics into power plant operations and maintenance is emerging as a key solution to address these challenges. While several utilities are utilizing third-party robotic technologies, there remains considerable scope for the development of indigenous solutions. NETRA, R&D wing of NTPC; the largest power utility of India, has taken significant steps toward developing such tools indigenously. As part of these efforts, NETRA has successfully developed multiple tools, including two developed in collaboration with CMERI, Durgapur. These tools have been effectively deployed across various power plants which demonstrate strong potential for future commercialization within the industry. This paper delves into these advancements and their implications for the sector.

Intelligent Inspection of Electronic Devices in Specific Environments via a Novel Cascade Network of Combining Mixed Sampling and Nonstrided Convolution

Intelligent Inspection of Electronic Devices in Specific Environments via a Novel Cascade Network of Combining Mixed Sampling and Nonstrided Convolution

Missing piece: tracheobronchial foreign body aspiration during an elective thyroidectomy

Airway management during thyroid surgery is challenging, particularly in patients with altered upper airway anatomy that may impair laryngoscopic visualisation. Bougies are commonly used to aid difficult intubation, but in rare cases, they can fracture and lead to tracheobronchial aspiration. We report the case of a woman in her mid-50s with multinodular goitre suspicious for malignancy who underwent elective thyroidectomy. Difficult intubation necessitated bougie use, but postoperatively, its tip was found missing. Chest CT confirmed the fragment lodged in the right lower lobe bronchus. The patient was transferred to our institution, where flexible bronchoscopy successfully retrieved the 2.5 cm fragment. Her cough resolved, and aside from post-thyroidectomy hypocalcaemia, her recovery was uneventful. Bougie fracture during intubation is extremely rare but should be considered in difficult airways. Early recognition, prompt imaging and bronchoscopy are crucial for management. Routine device inspection and careful procedural planning are key to preventing such complications.

Monitoring Dental Zinc Oxide Polyacrylate Cement Using Near-Infrared Spectroscopy

This study aims to investigate the reaction kinetics of polycarboxylate cements using NIR spectroscopy with chemometrics. To achieve 100% inspection of medical devices and drugs, it is desirable to develop non-destructive, non-contact quality control methods. One effective method for this is chemometrics, which uses near-infrared spectroscopy and chemometrics. A dental cement, polycarboxylate cement, was prepared, and the spectral changes from 1 min to 15 min were observed using NIR spectroscopy. The obtained spectra were evaluated by two-dimensional correlation spectroscopy. Carboxylate changes in the cement were analyzed using principal component analysis, and a score plot against loadings and time was shown. It was suggested that the hardening mechanism of cement can be plotted as a pseudo-first-order reaction.

Improving pressure relief device (PRD) inspection intervals through critical industrial accident analysis and field investigation in Korea

Abstract In Korea, the inspection and maintenance intervals for pressure relief devices (PRDs) are determined by the installation's purpose and the substances being handled. The law mandates inspection frequencies ranging from one to four years, depending on the associated economic and safety risks. This study aims to assess whether the current legal requirements for PRD inspection intervals can be made more flexible, drawing on empirical data from incident case analysis, site visits, and surveys. We examined PRD installation and operating conditions through site visits to 65 workplaces and surveys of 117 workplaces and stakeholders to identify challenges related to PRD inspection cycle regulations and their practical applicability. Based on these findings, we propose moving from a regulated to a self‐regulated PRD inspection cycle, in collaboration with industry experts and government agencies. The results of this study are intended to support improvements in chemical process safety management policies in Korea.

Key technologies of China high-speed comprehensive inspection train: CIT450

Abstract The China comprehensive inspection train (CIT) is designed for evaluating railway infrastructure to ensure safe railway operations. The CIT integrates an array of inspection devices, capable of simultaneously assessing railway health condition parameters. The CIT450, representing the second generation, can reach a top speed of 450 km/h with inspection on the infrastructure. This paper begins by outlining the global evolution of inspection trains. It then focuses on the critical technologies underlying the CIT450, which include: (1) real-time inspection data acquisition with spatial and temporal synchronization; (2) intelligent fusion and centralized management of multi-source inspection data, enabling remote supervision of the inspection process; (3) technologies in inspecting track, train–track interaction, catenary, signalling systems, and train operating environment; and (4) AI-driven analysis and correlation of inspection data. The future developmental directions for comprehensive inspection trains are discussed finally. The CIT450’s approach to real-time railway health monitoring can enrich traditional inspection means, operational, and maintenance methods by enhancing inspection efficiency and automating railway maintenance.

A weak bias-magnetized dynamic permeability testing method for detecting and distinguishing inner and outer diameter defects in gas pipelines

A weak bias-magnetized dynamic permeability testing method for detecting and distinguishing inner and outer diameter defects in gas pipelines

Research on Asphalt Pavement Surface Distress Detection Technology Coupling Deep Learning and Object Detection Algorithms

To address the challenges posed by the vast scale of highway maintenance in China and the high costs associated with traditional inspection vehicles. This study focuses on a routine maintenance project for national and provincial roads in Shanxi Province, with an emphasis on the selection and design of hardware for lightweight, portable pavement inspection devices. A monocular camera was used to capture pavement surface images, resulting in a dataset of 85,511 training samples. Additionally, the YOLOv5 object detection algorithm, combined with convolutional deep learning techniques, was employed to classify and identify pavement surface distresses in the collected images. Through multiple iterations of model tuning and validation, the proposed detection system achieved a false negative rate of 1.13%, a recall rate of 97.35%, and a precision rate of 98.30%. Its high accuracy provides a technical reference for the development and design of portable pavement distress detection devices.

Разработка алгоритма определения периодичности инспекции предохранительных устройств на основе качественного анализа риска

The most crucial aspect of the safe operation of a hazardous production facility is compliance with the industrial safety requirements established by the Federal Law № 116-FZ «On industrial safety of hazardous production facilities», federal norms and rules for industrial safety, and other documents that regulate ensuring industrial safety. Therefore, different types of devices, whose integrity and reliability must also be ensured, are used at hazardous production facilities. The matters of determination of inter-inspectional intervals are considered; Russian and international regulatory documents as to the inspection of safety devices are reviewed. The algorithm takes into account the following factors of safety device operation: working environment, corrosiveness, exposure of a safety device to vibration and pulsation, and the criticality of a safety device for personnel’s and production safety. The frequency of inspection of safety devices in accordance with the proposed algorithm does not contradict the maximum permissible intervals stipulated in API 510, API 576, and NBIC-2015; at the same time, conservatism and objectivity are preserved when determining the criticality. A direct-acting spring safety valve of direct action DN 25/50, PN20, used at hazardous production facilities of oil and gas refinery plants has been selected as an object of research. According to the calculations, the inter-inspectional interval under IPKM—2005 is 36 months (3 years), under API 510/576 is 120 months (10 years), and under the proposed algorithm is 48 months (4 years). Therefore, the proposed algorithm implies qualitative risk analysis that considers operating conditions of safety devices, which requires a high level of expertise and skills from inspecting persons. It is based on the risk assessment, which does not contradict the effective regulatory documents of the Russian Federation and therefore can be applied at hazardous production facilities. Determining frequency in accordance with the proposed algorithm will help improve the economic efficiency of operating companies and ensure an acceptable level of industrial safety.

Development of a portable device for structural visual inspection

Abstract Visual inspection is crucial for the maintenance of built infrastructures, facilitating early detection and quantification of damage. Traditional manual methods, however, often require inspectors to access dangerous or inaccessible areas, posing significant safety risks and inefficiencies. In response to these challenges, this paper introduces a portable visual inspection device (VID) integrated with three laser distance meters and a high‐resolution camera. The VID enhances the efficiency of visual inspection by incorporating methods that accurately estimate the camera's pose relative to the target surface and determine a scale factor for precise damage quantification. The proposed methods were validated through experimental validations, demonstrating their precision and effectiveness. In lab‐scale validation, the angle estimation showed accuracy with less than 3 degrees of error, and the scale factor estimation method showed discrepancies of less than 1 mm, even when the observation angle exceeded 20 degrees. Subsequent field experiments confirmed the VID's capability to detect and measure microcracks as narrow as 0.1 mm. Furthermore, the device successfully quantified non‐crack damage with an error margin of 1.84%, even at challenging angles exceeding 45 degrees.

From traditional robotic deployments towards assisted robotic deployments in nuclear decommissioning.

The history around teleoperation and deployment of robotic systems in constrained and dangerous environments such as nuclear is a long and successful one. From the 1940s, robotic manipulators have been used to manipulate dangerous substances and enable work in environments either too dangerous or impossible to be operated by human operators. Through the decades, technical and scientific advances have improved the capabilities of these devices, whilst allowing for more tasks to be performed. In the case of nuclear decommissioning, using such devices for remote inspection and remote handling has become the only solution to work and survey some areas. Such applications deal with challenging environments due to space constrains, lack of up-to-date structural knowledge of the environment and poor visibility, requiring much training and planning to succeed. There is a growing need to speed these deployment processes and to increase the number of decommissioning activities whilst maintaining high levels of safety and performance. Considering the large number of research and innovation being done around improving robotic capabilities, numerous potential benefits could be made by translating them to the nuclear decommissioning use cases. We believe such innovations, in particular improved feedback mechanisms from the environment during training and deployments (i.e., Haptic Digital Twins) and higher modes of assisted or supervised control (i.e., Semi-autonomous operation) can play a large role. We list some of the best practices currently being followed in the industry around teleoperation and robotic deployments and the potential benefits of implementing the aforementioned innovations.

Blockchain-integrated IoT device for advanced inspection of casting defects

The quality control of investment casting remains a critical challenge due to defect detection, real-time processing, and data traceability inefficiencies. This study presents an innovative Blockchain-integrated IoT system for advanced inspection of casting defects, combining a ResNet-based deep learning model for defect detection and dimensional measurement with Blockchain technology to ensure data integrity and traceability. The system demonstrated a significant improvement in defect detection accuracy, achieving an F1-score of 0.94, alongside high data integrity (0.99) and traceability (0.98) metrics. Additionally, it processes each casting in an average of 2.3 s, supporting a throughput of 26 castings per minute. By addressing critical challenges in smart manufacturing, this approach enhances operational efficiency, regulatory compliance, and user confidence. While scalability and energy efficiency remain areas for improvement, the proposed method provides a transformative solution for Industry 4.0, fostering transparency and reliability in manufacturing processes.

Совершенствование алгоритма оценки вероятности отказа по методике API RP 581 3rd Edition

The study considers the problem of improving the algorithm for assessing the failure probability of a process pipeline of gas condensate stabilization system of a hazardous production facility. The failure probability assessment is one of the basic components of a risk-oriented approach to the inspection of technical devices. The inspection, in turn, is a tool to ensure integrity, which is a necessary condition for industrial safety. According to the API RP 581 3rd Edition methodology, the equipment failure probability is assessed considering the degradation mechanism as well as design parameters and operating conditions. The generic failure frequency is a basic parameter of failure probability and determined for various types of equipment (vessels, tanks, pipelines) based on a vast volume of information on failures of the given type of equipment in the industry. The analysis of the practical use of the failure frequency index has shown that all the methodologies considered recommend using actual values of failure frequency indices. At the same time, there are some limitations, including the missing necessary comprehensive information base. A calculation model of dynamic registration of failures of technical devices aiming at the improvement of the algorithm of failure probability assessment based on the API RP 581 3rd Edition has been proposed. A detailed calculation procedure based on the suggested model has been provided; risk and inspection interval calculations have been performed as an example. The calculation results have been compared with conservative values; the increase of risk assessment objectivity using the dynamic model of failure frequency registration has been noticed. Improving the algorithm for assessing failure probability based on the API RP 581 3rd Edition methodology positively affects the process of ensuring a high level of industrial safety, including the integrity of technical devices.

Efficient net-based deep learning model for accurate plant disease classification and diagnosis

Diseases are a major drawback to crop production, productivity, and food security in nations affected by plant diseases. This work proposes an efficient framework for the automated recognition and diagnosis of diseases within plants using a convolutional neural network architecture known as EfficientNet. For this study, a large dataset containing sharp images of impaired and hale plant organs belonging to various species was collected. Common data preprocessing steps such as Resizing and Augmentation were used to reduce overfitting and increase the model’s ability to generalize. Finally, EfficientNet was trained for multi-class disease segmentation with the validation accuracy of 95%. The model showed high value of accuracy and recall and solved problems of the differentiation of visually similar diseases among the different categories. It is so from the following view: These results show the possibility of this approach as the practical tool for early disease detection and management in agriculture on large scale. Further studies are going to be conducted enlarging the data set, enhancing the transferability of the developed model, and examining how the app is best to be disseminated, for instance, via mobile applications or Internet of Things (IoT) devices for constant farming inspection.

Role of Establishment of Allowable Limits for Leachable Substances in Safety Evaluation of Medical Devices

The objective of inspection, testing and supervision of medical devices is to ensure the effectiveness and safety of medical devices in use. Leachables are substances that are leached from medical devices or materials during their clinical use. Leachables are important factors for the safety risks of medical devices. The analysis, detection, and safety evaluation of leachables are important parts of the safety evaluation of medical devices. The allowable limits for leachable substances which are established on the toxicological research provide a scientific basis for the judgment of qualitative and quantitative analysis results. Obtaining more detailed, rigorous and sufficient toxicological research data is of great significance to set highly enforceable product technical indicators. For the establishment of allowable limits for leachable substances, its role in the safety evaluation of medical devices is summarized, and the relevant standards and their implementation status in the testing of medical devices are introduced.

ВОЗМОЖНОСТИ ИСПОЛЬЗОВАНИЯ СОЦИАЛЬНЫХ СЕТЕЙ ПРИ РАССЛЕДОВАНИИ ПРЕСТУПЛЕНИЙ

In the modern information society, social networks occupy an important place in the daily lives of millions of people. These platforms provide ample opportunities for communication, information exchange and self-expression, but at the same time create new challenges for law enforcement agencies. The article examines the use of social networks in the process of investigating crimes, focusing on the legal aspects and practical methods of obtaining investigative and evidentiary information. A wide range of tools used to extract data from social networks is considered, ranging from official requests to administrators of social networks and ending with the inspection and seizure of electronic devices. The process of monitoring user activity is highlighted, which makes it possible to identify potential threats and collect important evidence. Attention is also paid to the specifics of working with open sources of information, such as public posts, comments, and photo albums. The article pays special attention to the problems related to the use of social networks in investigative practice. Practical recommendations on the effective use of social networks in the course of crime investigation are given, as well as prospects for further development of this area are considered.

Acoustic Signal‐Based Deep Learning Approach and Device for Detecting Interfacial Voids in Steel–Concrete Composite Structures

The reliable synergy between steel and concrete is an important evaluation criterion for the safety and long‐term use of steel–concrete composite structures (SCCSs). However, it is still a great challenge to realize fast and automated interface void detection for composite structures such as towers. Therefore, the experiments of void detection of the bridge tower full‐scale model were carried out with the background of Zhang Jinggao Yangtze River Bridge and other composite structure bridge towers. An automated inspection robotic device was developed, and a convolutional neural network (CNN) identification and classification model based on excitation vibration acoustic signals for the detection of void damage was proposed. In addition, the applicability of the response characteristics of the acoustic signal to the model and the visualization of the features were discussed. Developed tracked magnetic inspection robots can be applied to a variety of towering and complex confined areas, with real‐time inspection efficiency as high as 0.1 m2/s. The method based on excitation vibration acoustic signal analysis can be used as a new method for the detection of void damage in composite structures dependent on automatic inspection devices. The introduction of Mel spectrum features into the analysis of excitation vibration acoustic signals can improve the accuracy of identifying structural void damage. Compared with the conventional MLP and LSTM neural network models, the constructed Mel Spectrum with CNN model can realize high‐precision classification of damage, health, and invalid data of the composite structural interface, and the classification and recognition accuracy reaches 96.8%. The equipment and method can realize the accurate and fast detection of the interface void of the towering composite structure. It improves the automation of the void detection process and reduces the safety risk of the detection.

Analysis of bottleneck technology identification and development characteristics in the electronic manufacturing industry.

In 2018, US government imposed a ban on Chinese electronics firm ZTE, prohibiting it from procuring chip products from American companies for a duration of seven years. Subsequently, ZTE experienced a shutdown. The concept of "bottleneck technology" gained prominence in diverse media outlets. Chinese government acknowledged the importance of identifying pertinent bottleneck technologies in specific sectors, particularly in electronic manufacturing. It underscored the significance of promptly analyzing the evolutionary traits of these technologies within the industry and engaging in research and development endeavors to safeguard national technological security. To address this issue, the study initially extracted pertinent industry enterprises from the Orbis database. Subsequently, it utilized the technical codes associated with the patent data of these enterprises to establish a social network, pinpoint key technologies, and compute the technical share metrics for these pivotal technology codes. Numerous technologies are exclusively controlled by a limited number of enterprises, leading to the identification of bottleneck technologies. This, in turn, enables the determination of critical paths to reveal the developmental history and distinctive features of these bottlenecks. The research indicates that the electronic manufacturing industry faces various bottleneck technologies, which extend beyond chips to include production equipment, materials, processes, and inspection devices. Numerous technologies have a significant developmental background, necessitating substantial technological development to achieve parity. However, in swiftly advancing technological domains, China possesses the capability to make advancements at an accelerated rate.