Inspection Technology Research Articles

Advanced Ultrasonic Testing and post-processing for detection of sustainable composites

Carbon-fibre reinforced composites (CFRP) and glass-fibre reinforced composites (GFRP) has seen widespread usage in various industries, from aerospace to utilities. With the 'The green shift' and sustainability movement gaining momentum, there is an increased pressure for sustainable / natural materials to be used. Natural materials such as flax fibre can be used as a replacement for glass fibres. With the expectation that these composites will see increased usage, there is a need to develop inspection technologies. In this paper, single element immersion ultrasonic testing is used to inspect the flax fibre composites. The initial step involved inspection optimisation to obtain ideal input parameters. The raw results had no clear detection of the seeded delamination (using Teflon sheets). An advanced post-processing technique used for fibre waviness detection in CFRP was applied to the data. This post-processing technique included frequency analysis, phase analysis and amplitude analysis. The three types of data were separated from the raw data and visualised using a C-scan. Using this post-processing method, the seeded delamination could be detected. As a follow-up, more samples of various thicknesses and varying values of fibre waviness were fabricated to understand the limitation of inspection of flax fibres composites. This was achieved by having different thicknesses of Teflon sheets. The aim is to eventually have an inspection technique for a natural composite. This inspection technique and post-processing techniques has the potential to be applied onto the composite fabrication process, to aid in the manufacturing, optimisation, and validation process.

A modified common midpoint approach for GPR radars

Ground Penetrating Radar (GPR) plays an important role among the non-destructive methods used to analyze, measure and collect data from pavement layers, building structures and archaeological sites. A GPR device consists of a radar able to get an image, technically called radargram, of the subsurface. Due to the decreasing costs of calculation power, it is now possible to analyze and interpret radargrams more efficiently than in past. While technological advancements and improvements in the post-processing of data have increased the power of this process, it is also important to consider survey technique as a key factor in nondestructive testing. The aim of this paper is to propose a modification of a well-known survey technique called Common Mid-Point (CMP). The conventional CMP survey technique requires that the transmitter and receiver of the GPR device be physically separated to survey a site. This provides the benefit of determining the speed of light in the host medium from the slant of the hyperbola reflection of a buried target. The CMP process unifies the inspection technology into a single device, making the use of GPR more accessible to robots on construction sites. The method documented in this paper is based on a mathematical model for adapting the conventional CMP for conventional GPR radar devices, in which transmitter and receiver cannot be separated physically.

A construction and inspection technology of power emergency communication system based on Beidou

In order to improve the real-time performance of information transmission of power emergency communication system, the construction and inspection technology of power emergency communication system based on Beidou is proposed in this paper. Firstly, the differences between heterogeneous networks are described based on Beidou short message. Then, the fusion probability of heterogeneous network is calculated, and the optimal communication channel is obtained. Next, communication channel link is established, and the maximum transmission range is described. In addition, the network capacity is calculated, the congestion avoidance model is established, the bandwidth is obtained by defining the interference index of the information transmission channel, the total load during the construction and inspection process is calculated, and the construction and inspection of the power emergency communication system are realized.. Experimental results show that the proposed technology has better performance in information utilization, network throughput and system response time.

Research on Non-Contact Electricity Inspection Technology of Unmanned Aerial Vehicles

The airborne non-contact electroscope developed in this paper does not need to touch the tower and can be remotely controlled to complete the electroscope. It can be widely used in the maintenance operation of various voltage levels, and the entire operation process is in line with the State Grid Corporation of China Electric Power Safe work regulations (power line part) regulations. The application of the device can effectively reduce the labor intensity of the operators, improve the operation efficiency, and ensure the safety of the operation.

Development and Prospect of Automatic Inspection System for Fabric Defects

Summarize the development of automatic inspection system for fabric defects. The main problems existing in traditional manual cloth inspection are pointed out. The development of automatic cloth inspection technology at home and abroad is reviewed. The classification and detection requirements of fabric defects are introduced. This paper probes into the functions and related technologies of automatic cloth inspection system, and looks forward to the prospect of automatic cloth inspection technology. It is considered that the use of automatic cloth inspection machine can improve labor productivity; Automatic cloth inspection machine is an important measure to upgrade the textile industry and get rid of labor-intensive industries, and it is an inevitable trend of future development.

YOLO-T: Multitarget Intelligent Recognition Method for X-ray Images Based on the YOLO and Transformer Models

X-ray security inspection processes have a low degree of automation, long detection times, and are subject to misjudgment due to occlusion. To address these problems, this paper proposes a multi-objective intelligent recognition method for X-ray images based on the YOLO deep learning network and an optimized transformer structure (YOLO-T). We also construct the GDXray-Expanded X-ray detection dataset, which contains multiple types of dangerous goods. Using this dataset, we evaluated several versions of the YOLO deep learning network model and compared the results to those of the proposed YOLO-T model. The proposed YOLO-T method demonstrated higher accuracy for multitarget and hidden-target detection tasks. On the GDXray-Expanded dataset, the maximum mAP of the proposed YOLO-T model was 97.73%, which is 7.66%, 16.47%, and 7.11% higher than that obtained by the YOLO v2, YOLO v3, and YOLO v4 models, respectively. Thus, we believe that the proposed YOLO-T network has good application prospects in X-ray security inspection technologies. In all kinds of security detection scenarios using X-ray security detectors, the model proposed in this paper can quickly and accurately identify dangerous goods, which has broad application value.

The RCNDE industrial members' 2022 vision for the future requirements of NDE

The UK Research Centre in Non-Destructive Evaluation (RCNDE), established in 2003, has become a leading industrial-academic collaboration for undertaking industrially relevant research in the fields of non-destructive evaluation (NDE) and structural health monitoring (SHM). The industrial members produced their first five-, ten- and 20-year vision for NDE in 2011 and updated it in 2016. This article describes the newly updated vision for NDE that was produced by the industrial members in 2021/2022. The overall vision presented in this article is a consolidation of the individual industrial sector visions, a process that is possible due to the fact that many longer-term objectives are common across sectors. This enables research projects to be defined that appeal to multiple industries and demonstrates the power of collaboration to deliver relevant solutions. To aid understanding, the responses have been grouped into three themes: improvements desired in the capabilities of NDE technologies; asset-based inspection challenges that need to be met; and aspects that deliver and help to realise the benefits of Industry 4.0. The vision produced by RCNDE's industrial members will be used to shape the core research programmes and identify opportunities for new collaborations. It will help to bring forward the application of inspection and monitoring technologies to meet future industrial needs and timescales.

Photometric-Stereo-Based Defect Detection System for Metal Parts.

Automated inspection technology based on computer vision is now widely used in the manufacturing industry with high speed and accuracy. However, metal parts always appear in high gloss or shadow on the surface, resulting in the overexposure of the captured images. It is necessary to adjust the light direction and view to keep defects out of overexposure and shadow areas. However, it is too tedious to adjust the position of the light direction and view the variety of parts' geometries. To address this problem, we design a photometric-stereo-based defect detection system (PSBDDS), which combines the photometric stereo with defect detection to eliminate the interference of highlights and shadows. Based on the PSBDDS, we introduce a photometric-stereo-based defect detection framework, which takes images captured in multiple directional lights as input and obtains the normal map through the photometric stereo model. Then, the detection model uses the normal map as input to locate and classify defects. Existing learning-based photometric stereo methods and defect detection methods have achieved good performance in their respective fields. However, photometric stereo datasets and defect detection datasets are not sufficient for training and testing photometric-stereo-based defect detection methods, thus we create a photometric stereo defect detection (PSDD) dataset using our PSBDDS to eliminate gaps between learning-based photometric stereo and defect detection methods. Furthermore, experimental results prove the effectiveness of the proposed PSBBD and PSDD dataset.

Intelligent inspection technology of protection device based on convolution neural network image recognition algorithm

Intelligent inspection technology of protection device based on convolution neural network image recognition algorithm

Optical Rail Surface Crack Detection Method Based on Semantic Segmentation Replacement for Magnetic Particle Inspection.

Railway damage detection is of great significance in ensuring railway safety. The cracks on the rail surface play a key role in studying the formation and development process of rail damage, predicting the occurrence of rail defects, and then improving the service life of the rail. However, due to the small shape of the cracks, the typical detection method is relatively complicated, and the speed is quite slow. Although traditional magnetic particle inspection technology is fairly accurate at detection, it is costly and inconvenient to carry and install, while also limiting the detection speed and affecting the system's operation. In this paper, a semantic segmentation detection method is developed by using various collected rail surface crack data and deep learning through a neural network. By comparing the inspection of the same rail surface with magnetic particle inspection technology, only inexpensive cameras are used and the inspection speed is increased while maintaining relatively high accuracy. In addition, the method can achieve fast detection speeds if it is extended to be combined with high-frequency cameras. It is an economical, efficient, and environmentally friendly method for future rail surface detection.

Multi-Sensor Image Fusion Method for Defect Detection in Powder Bed Fusion.

Multi-sensor defect detection technology is a research hotspot for monitoring the powder bed fusion (PBF) processes, of which the quality of the captured defect images and the detection capability is the vital issue. Thus, in this study, we utilize visible information as well as infrared imaging to detect the defects in PBF parts that conventional optical inspection technologies cannot easily detect. A multi-source image acquisition system was designed to simultaneously acquire brightness intensity and infrared intensity. Then, a multi-sensor image fusion method based on finite discrete shearlet transform (FDST), multi-scale sequential toggle operator (MSSTO), and an improved pulse-coupled neural networks (PCNN) framework were proposed to fuse information in the visible and infrared spectra to detect defects in challenging conditions. The image fusion performance of the proposed method was evaluated with different indices and compared with other fusion algorithms. The experimental results show that the proposed method achieves satisfactory performance in terms of the averaged information entropy, average gradient, spatial frequency, standard deviation, peak signal-to-noise ratio, and structural similarity, which are 7.979, 0.0405, 29.836, 76.454, 20.078 and 0.748, respectively. Furthermore, the comparison experiments indicate that the proposed method can effectively improve image contrast and richness, enhance the display of image edge contour and texture information, and also retain and fuse the main information in the source image. The research provides a potential solution for defect information fusion and characterization analysis in multi-sensor detection systems in the PBF process.

The Role of Industry 4.0 Technologies in Manufacturing Sustainability Assessment

Abstract To understand the past successes and future opportunities for applying Industry 4.0 technologies toward manufacturing sustainability assessment, this state-of-the-art review examines previous literature at the intersection of these domains. Specifically, we focus on the application of Industry 4.0 technologies in the context of the following stages in manufacturing sustainability assessment: (i) planning, infrastructure development, and problem definition, (ii) performance measurement, (iii) results interpretation and decision-making, as well as (iv) intervention, control, and validation strategies. On the basis of the literature review, we present the trends, benefits, research gaps, and required future work for holistically integrating the research domains mentioned above. To accompany this literature review, we performed a meta-analysis of 14,498 articles and compared them to 316 articles compiled from a focused literature search. Based on database-controlled keywords, networks representing keyword co-occurrences were constructed to reveal clusters of related terms and evaluate overall term centrality (i.e., importance). Results from this analysis showed that the two datasets exhibited a similar network structure and also helped reveal Industry 4.0-related opportunities for manufacturing sustainability assessment. They include areas such as automation, robotics, and advanced inspection technologies, which are yet to be exploited in manufacturing sustainability assessment. Further research is needed to investigate whether the incorporation of such areas can (i) facilitate more robust and accessible assessments of manufacturing sustainability and (ii) make manufacturing systems themselves more sustainable.

THz Super-Resolution Imaging in Transmission Technology by Using Butterfly and Pattern Device Samples

Recently there has been an increasing demand for terahertz technology, especially in imaging. In the past few decades, the applications of terahertz (THz) imaging technology have seen significant developments in the fields of biology, medical diagnosis, food safety, and nondestructive testing. The medical and semiconductor industry has always attracted significant attention worldwide. In particular, the importance of real and perfect inspection technologies has been growing due to an increasing demand for improving the quality of life and developing industries. This paper presents the research of THz super-resolution imaging in transmission mode by using different samples. We have reported transmission measurement at different THz frequency of each sample. The butterfly sample used super-resolution THz imaging. The THz super-resolution is obtained excellent at 1.8 THz, it is near about 1 micrometer. Good resolution images have been obtained. This new THz super-resolution techniques can apply in medical and security purposes. Further applications will be reported in the coming papers.

Inspection Technology of Power Communication Network Based on Machine Vision Graphic Recognition

With the continuous improvement of maintenance management level and the continuous progress of fault diagnosis technology, equipment condition maintenance has also gradually entered the stage of market use. Especially, in the electric power industry, with the development of the research and production practice of condition maintenance theory, its application areas are becoming more and more extensive. In recent years, with the increasing popularity of computer network technology, the development of communication network monitoring technology has also made great progress. However, the monitoring of communication equipment in China is still in the primary stage, and the complexity of the equipment and the diversity of the equipment make the research on its condition detection a very challenging task. The study introduces the application of computer vision-based graphic recognition technology in power communication networks, which includes two modules: FasterR-CNN and RPN. The model provides real-time monitoring of various performance indicators of power communication network equipment and feedback on its working status, repairs the equipment according to the monitoring results, timely detects potential safety hazards, and makes a maintenance cycle reasonable planning, ensuring the normal operation of the communication network.

Review of surface defect detection of steel products based on machine vision

Steel plays an important role in industry, and the surface defect detection for steel products based on machine vision has been widely used during the last two decades. This paper attempts to review state-of-art of vision-based surface defect inspection technology of steel products by investigating about 170 publications. This review covers the overall aspects of vision-based surface defect inspection for steel products including hardware system, automated vision-based inspection method, existing problems and latest development. The types of steel product surface defects composition of visual inspection system are briefly described, and image acquisition system is introduced as well. The image processing algorithms for surface defect detection of steel products are reviewed, including image pre-processing, region of interest (ROI) detection, image segmentation for ROI, feature extraction and selection and defect classification. The important problems such as small sample and real time of steel surface defect detection are discussed. Finally, the challenge and development trend of steel surface defect detection are prospected.

Research on Protection Test and Inspection Technology of Intelligent Traction Substation

Research on Protection Test and Inspection Technology of Intelligent Traction Substation

Insulator fault feature extraction system of substation equipment based on machine vision

AbstractThe artificial intelligence technology and intelligent automation are more and more widely used, the insulators play a supporting and insulating role in the operation of the grid. The use of machine vision inspection technology to detect insulator faults has become an inevitable trend of the times. In this study, based on BP neural network, the fault feature extraction system of substation insulator is studied, and then the system has some shortcomings in target detection algorithm. It then introduced two target detection algorithms, YOLOv3 and Canny, to improve the system. This study then analyses and compares the various indicators. It also analyses some external interference factors that exist when detecting insulator faults. It then further analyses the slenderness ratio of the smallest circumscribed rectangle of the insulator and the background region and the duty cycle of the insulator and the background region. The experimental results show that the recognition rates of both YOLOv3 and Canny target detection algorithms are around 90%. In this study, the influence of external factors is considered, and the fault features are further introduced, so that the true positive rate and accuracy of the algorithm have been improved.

Image-Based Precision Measurement Technology for the Quality Inspection of Crane Boom Materials

With the slow rise of the construction industry, cranes, as indispensable mechanical equipment in construction projects, are widely used in the lifting and handling of specific space ranges of construction projects. The quality of crane booms is particularly important for safety. This paper uses image measurement methods including filter processing, mean filtering, and Gaussian filtering to detect the quality of the crane boom material. The image is processed by the wavelet transform and Fourier transform. The grayscale transformation stretching method is applied to the surface image analysis of the boom material to obtain the final inspection. The research results show that the use of image measurement methods can effectively measure the thickness of the crane boom material, the geometric information of the boom material, and the surface roughness of the material and obtain effective image information. The detection accuracy reaches 98.1%. The error can be controlled better. The inspection and research on the quality of crane jib materials can ensure the quality and performance of crane jib materials, reduce the potential safety hazards of cranes during operation, and improve safety. This article organically combines workpiece surface roughness detection with digital image processing technology to preprocess the surface picture of the arm tube material. On this basis, the texture features in the image are extracted and programmed to calculate, and the final workpiece is obtained by the surface roughness value, which proves the feasibility of this method. The research results have very important practical significance for the detection of the quality of the arm tube material and the improvement of the quality level of the arm tube material.

The Use of Novel, Rapid Analytical Tools in the Assessment of the Stability of Tablets—A Pilot Analysis of Expired and Unexpired Tablets Containing Nifuroxazide

In the analysis of finished pharmaceutical products, numerous innovative analytical techniques are often used, i.e., Raman spectroscopy, scanning electron microscopy, computer microtomography, directional hemispherical reflectance, and hyperspectral analyses. These techniques allow for the identification of changes in solid phases. Many advantages over other techniques can be attributed to these techniques, e.g., they are rapid, non-destructive, and comprehensive. They allow for the identification of changes occurring in solid phases. However, the above-mentioned methods are still not standard procedures in pharmaceutical research. The present study aimed to assess the possible usefulness of total directional hemispherical reflectance (THR), hyperspectral imaging, and computer microtomography to evaluate the stability of tablets containing nifuroxazide during storage. In the study, expired and unexpired coating tablets containing nifuroxazide (n = 10 each) were analyzed. In addition, four unexpired tablets were stored at 40°C over 3 months (stressed tablets). Reflectance was determined with seven wavelength bands from 335 nm to 2500 nm using an SOC-410 Directional Hemispherical Reflectometer (Surface Optics Corporation, San Diego, CA, USA). A Specim IQ hyperspectral camera (Spectral Imaging Ltd., Oulu, Finland) was used with a wavelength range of 400–1030 nm. Tablets were also scanned using X-ray microtomography (Phoenix vǀtomeǀx, GE Sensing & Inspection Technologies GmbH, Wunstorf, Germany). The results indicated that total reflectance was lower in expired tablets than in unexpired tablets in all spectral bands, except for 700–1100 nm and 1700–2500 nm. In turn, the stressed tablets showed higher THR values than expired tablets in all spectral bands, except for 1000–1700 nm. In addition, hyperspectral analysis of the homogeneity of the tablets, as well as X-ray microtomographic analysis of tablet density and coating thickness, indicated that these parameters differed significantly between the analyzed tablets.

Smart Sensor Network Optimization and Line Defect Identification Based on UAV Transmission Inspection under 5G Technology

Transmission line is a key link in the transmission line, as the main equipment in the grid are located in the field; in the long-term harsh environmental conditions, the line is very easy to appear broken strands, broken, and other defects, which need to be regularly monitored and maintained. The traditional manual line inspection operation is not only harsh, labor-intensive, inefficient, and low precision but also has a great impact on the personal safety of the operators due to the complicated operating conditions. This project mainly focuses on 5G, intelligent sensor network technology, and the application of UAVs in high-voltage line inspection, detection, and maintenance. The developed inspection UAVs can effectively improve the efficiency of line inspection and quickly discover fault points, which brings convenience to the construction of power systems. In the 5G environment, the use of UAV transmission inspection technology for line fault identification can overcome the shortcomings of manual faults, but also timely and comprehensive fault detection, and improve the effectiveness and standardization of monitoring work.