Inspection System Research Articles

Design of intelligent inspection system for solder paste printing defects based on improved YOLOX

Aiming at the current SPI (solder paste inspection) system for printing solder paste similar defects detection accuracy is not high, the system intelligence degree is low and so on, design a for the solder paste similar defects and combined with phase modulation profile measurement technique and improve the YOLOX intelligent detection system. The core of the system is the improved YOLOX depth model based on s-mosica and kt-iou algorithms proposed in this paper. The experimental results show that the proposed s-mosica and kt-iou algorithms can effectively improve the detection accuracy of printed solder paste, and when combined with the YOLOX model, the best 90.33% detection accuracy is obtained, which is better than the detection performance of the existing algorithms in the same scenario, and it provides an effective and feasible reference program for the design of the SPI high-precision intelligent detection system.

Design and Implementation of Automatic Airport Inspection System of Vehicle-mounted Rotor UAV

A distributed UAV automatic airport inspection system, with the advantages of unmanned autonomous inspection operation, is widely used in power patrol, highway inspection, forest fire monitoring, and other fields. The disadvantage is that the operating radius distance is limited, with the multi-fixed site layout and high cost. In this paper, a vehicle-mounted rotor UAV automatic airport inspection system is designed, which can flexibly move the operation site and increase the operation radius. This inspection system is based on 5G wireless communication control technology and can remotely control the UAV. After the operation is completed, the UAV is automatically stored, charged, and maintained, and other functions have a good application prospect.

Design and Implementation of a Full-Time Artificial Intelligence of Things-Based Water Quality Inspection and Prediction System for Intelligent Aquaculture

Design and Implementation of a Full-Time Artificial Intelligence of Things-Based Water Quality Inspection and Prediction System for Intelligent Aquaculture

Dynamic Self Checking Technology of Transformer Device in Intelligent Power Plant Based on Protection Logic Optimization

So as to ensure the normal operation of transformer device and reduce the heavy loss caused by fault, a dynamic self-test skill of transformer device in smart muscle vegetation under the optimization of protection logic is proposed. The protection logic optimization scheme is proposed, and the single column three winding architecture of the main transformer of the smart muscle vegetation transformer device is constructed. On the basis of this architecture, according to the four arm thermal conductivity pool architecture and the wavelet denoising process, the performance self inspection system of the smart muscle vegetation transformer device is designed. According to the load model of the smart muscle vegetation transformer, Thus, the dynamic self-test skill of smart muscle vegetation transformer device under the protection logic optimization is realized. The laboratorial consequences show that the frequency response curves of the faulty winding and the winding under normal operation are basically the same, which can accurately diagnose the short circuit fault between turns of the transformer, and the self-test skill has strong test sensitivity.

Calculations of Temperature and Thermo-Elastic Displacement Distributions Generated by Laser-Ultrasonic Inspection System

Calculations of temperature and thermo-elastic displacement distributions generated by laser-ultrasonic inspection system are performed. The goal is to determine temperature distributions created in the test objects by generation laser and the respective thermo-elastic displacement distributions causing the ultrasonic waves propagating within the test objects during laser-ultrasonic inspection of parts. The respective heat propagation equation and thermo-elastic equation of motion are solved analytically for 1D and 3D test objects with different geometries.

A Pavement Crack Detection and Evaluation Framework for a UAV Inspection System Based on Deep Learning

Existing studies often lack a systematic solution for an Unmanned Aerial Vehicles (UAV) inspection system, which hinders their widespread application in crack detection. To enhance its substantial practicality, this study proposes a formal and systematic framework for UAV inspection systems, specifically designed for automatic crack detection and pavement distress evaluation. The framework integrates UAV data acquisition, deep-learning-based crack identification, and road damage assessment in a comprehensive and orderly manner. Firstly, a flight control strategy is presented, and road crack data are collected using DJI Mini 2 UAV imagery, establishing high-quality UAV crack image datasets with ground truth information. Secondly, a validation and comparison study is conducted to enhance the automatic crack detection capability and provide an appropriate deployment scheme for UAV inspection systems. This study develops automatic crack detection models based on mainstream deep learning algorithms (namely, Faster-RCNN, YOLOv5s, YOLOv7-tiny, and YOLOv8s) in urban road scenarios. The results demonstrate that the Faster-RCNN algorithm achieves the highest accuracy and is suitable for the online data collection of UAV and offline inspection at work stations. Meanwhile, the YOLO models, while slightly lower in accuracy, are the fastest algorithms and are suitable for the lightweight deployment of UAV with online collection and real-time inspection. Quantitative measurement methods for road cracks are presented to assess road damage, which will enhance the application of UAV inspection systems and provide factual evidence for the maintenance decisions made by road authorities.

A Collaborative Visual Sensing System for Precise Quality Inspection at Manufacturing Lines

Visual sensing has been widely adopted for quality inspection in production processes. This paper presents the design and implementation of a smart collaborative camera system, called BubCam , for automated quality inspection of manufactured ink bags in Hewlett-Packard (HP) Inc.’s factories. Specifically, BubCam estimates the volume of air bubbles in an ink bag, which may affect the printing quality. The design of BubCam faces challenges due to the dynamic ambient light reflection, motion blur effect, and data labeling difficulty. As a starting point, we design a single-camera system which leverages various deep learning (DL)-based image segmentation and depth fusion techniques. New data labeling and training approaches are proposed to utilize prior knowledge of the production system for training the segmentation model with a small dataset. Then, we design a multi-camera system which additionally deploys multiple wireless cameras to achieve better accuracy due to multi-view sensing. To save power of the wireless cameras, we formulate a configuration adaptation problem and develop the single-agent and multi-agent deep reinforcement learning (DRL)-based solutions to adjust each wireless camera’s operation mode and frame rate in response to the changes of presence of air bubbles and light reflection. The multi-agent DRL approach aims to reduce the retraining costs during the production line reconfiguration process by only retraining the DRL agents for the newly added cameras and the existing cameras with changed positions. Extensive evaluation on a lab testbed and real factory trial shows that BubCam outperforms six baseline solutions including the current manual inspection and existing bubble detection and camera configuration adaptation approaches. In particular, BubCam achieves 1.3x accuracy improvement and 300x latency reduction, compared with the manual inspection approach.

Design Of Charge and Discharge Performance Inspection System for Lead-Acid Battery in Coal Mine

On the basis of the introduction of coal mine safety production situation and coal mine lead-acid battery, we designed the coal mine lead-acid battery charging and discharging performance inspection system, focusing on the design of the charging or discharging test voltage detection circuit, current detection circuit and IGBT isolation drive circuit, to realize the inspection of the charging and discharging performance , battery capacity and other test items of lead-acid battery in coal mine.

An autonomous Unmanned Aerial Vehicle exploration platform with a hierarchical control method for post‐disaster infrastructures

AbstractCatastrophic natural disasters like earthquakes can cause infrastructure damage. Emergency response agencies need to assess damage precisely while repeating this process for infrastructures with different shapes and types. The authors aim for an autonomous Unmanned Aerial Vehicle (UAV) platform equipped with a 3D LiDAR sensor to comprehensively and accurately scan the infrastructure and map it with a predefined resolution r. During the inspection, the UAV needs to decide on the Next Best View (NBV) position to maximize the gathered information while avoiding collision at high speed. The authors propose solving this problem by implementing a hierarchical closed‐loop control system consisting of a global planner and a local planner. The global NBV planner decides the general UAV direction based on a history of measurements from the LiDAR sensor, and the local planner considers the UAV dynamics and enables the UAV to fly at high speed with the latest LiDAR measurements. The proposed system is validated through the Regional Scale Autonomous Swarm Damage Assessment simulator, which is built by the authors. Through extensive testing in three unique and highly constrained infrastructure environments, the autonomous UAV inspection system successfully explored and mapped the infrastructures, demonstrating its versatility and applicability across various shapes of infrastructure.

Analysis of the Application of Bridge Rapid Inspection and Evaluation Technology

Rapid bridge inspection and evaluation mainly uses information technology to test the quality of bridge infrastructure and structures, integrates the test results with the existing management system, completes the bridge status assessment, establishes information management files to provide bridge disease problem inspection and analysis, and provides support for the application of disposal measures. This paper briefly discusses the necessity of applying rapid inspection and evaluation technology and analyzes the bridge’s rapid inspection and evaluation content, inspection system, and application process. We look forward to the future application prospects of this technology and supporting those in this field.

Study on the impact of central environmental protection inspection on the health of the older adult population-A quasi-natural experiment in China.

In 2015, the central government of China established the Central Environmental Protection Inspection (CEPI) system for oversight of local governments. It enhanced local government enforcement of environmental regulations, which had a considerable influence on the health of the local older adult population. This study quantifies the effects of local government regulation brought about by CEPI on the health of the older adult. It examines the impact mechanism using the DID model and panel data from the China Health and Retirement Longitudinal Study (CHARLS). The results show that (1) local governments' environmental protection regulations implemented by CEPI have a positive impact on the general health of the older adult. The results of the study passed the parallel trend test, PSM test, replacement variable test, and placebo test and remained significant; (2) in terms of the impact mechanism, CEPI has promoted local governments' environmental governance initiatives, which has reduced industrial wastewater emissions, industrial sulfur dioxide emissions, and industrial fumes emissions. This has improved air quality, thereby creating a good living environment for the older adult and improving their overall health; (3) according to heterogeneity research, the health of older adult living in the Yangtze River Basin, urban older adult, and older adult without chronic diseases is more significantly affected by the environmental protection regulations of the local governments brought about by CEPI.

Automated quality inspection of baby corn using image processing and deep learning

The food industry typically relies heavily on manual operations with high proficiency and skills. According to the quality inspection process, a baby corn with black marks or blemishes is considered a defect or unqualified class which should be discarded. Quality inspection and sorting of agricultural products like baby corn are labor-intensive and time-consuming. The main goal of this work is to develop an automated quality inspection framework to differentiate between ‘pass’ and ‘fail’ categories based on baby corn images. A traditional image processing method using a threshold principle is compared with relatively more advanced deep learning models. Particularly, Convolutional neural networks, specific sub-types of deep learning models, were implemented. Thorough experiments on choices of network architectures and their hyperparameters were conducted and compared. A Shapley additive explanations (SHAP) framework was further utilized for network interpretation purposes. The EfficientNetB5 networks with relatively larger input sizes yielded up to 99.06% accuracy as the best performance against 95.28% obtained from traditional image processing. Incorporating a region of interest identification, several model experiments, data application on baby corn images, and the SHAP framework are our main contributions. Our proposed quality inspection system to automatically differentiate baby corn images provides a potential pipeline to further support the agricultural production process.

Intelligent reflecting surface‐assisted UAV inspection system based on transfer learning

AbstractIntelligent reflective surface (IRS) provides an effective solution for reconfiguring air‐to‐ground wireless channels, and intelligent agents based on reinforcement learning can dynamically adjust the reflection coefficient of IRS to adapt to changing channels. However, most exiting IRS configuration schemes based on reinforcement learning require long training time and are difficult to be industrially deployed. This paper, proposes a model‐free IRS control scheme based on reinforcement learning and adopts transfer learning to accelerate the training process. A knowledge base of the source tasks has been constructed for transfer learning, allowing accumulation of experience from different source tasks. To mitigate potential negative effects of transfer learning, quantitative analysis of task similarity through unmanned aerial vehicle (UAV) flight path is conducted. After identifying the most similar source task to the target task, parameters of the source task model are used as the initial values for the target task model to accelerate the convergence process of reinforcement learning. Simulation results demonstrate that the proposed method can increase the convergence speed of the traditional DDQN algorithm by up to 60%.

A Structural Health Monitoring System for Bond Line Flaws Detection on a Full-Scale Wingbox Section Demonstrator

In recent years, there has been a significant increase in the use of structural health monitoring (SHM) technologies as systems for monitoring the integrity of aircraft’s structures. The use of compact and embeddable sensor networks, like the ones based on fibre optics (FO), is particularly attractive from the perspective of releasing an integrated structural system with intrinsic sensing capacity. Usually, an SHM system architecture is completed by a dedicated algorithm that processes the data gathered from the sensors to elaborate on the level of damage currently suffered by the structure, with the further possibility of providing information to the relevant specialists involved with its supervision. One of the main SHM applications that is attracting major interest is related to the inspection and detection of anomalies in bonded joints, which is extremely relevant in many composite realizations. Aeronautical regulations allow the use of bonded joints on an aircraft’s primary structure but require the implementation of a means to ensure their absolute safety, such as the introduction of further mechanical links aimed at stopping the propagation of a possible flaw or the availability of Non-Destructive Inspection (NDI) systems to prove the absence of relevant damaged areas. Generally, the main typical defects occurring during the manufacturing of bonded joints include adhesive curing, kissing bonds, poor porosity, and poor surface preparation. The current NDI systems more widely used and available to detect defects are still inaccurate due to the lack of standard procedures for the creation of representative defects in a controlled manner, which would allow for the development of reliable methodologies and tools able to ensure the safety of a bonded joint, as required by safety regulations. This paper shows the results relative to the implementation of an SHM system developed by the Italian Aerospace Research Centre (CIRA) aimed at monitoring the bonding lines between spar caps and panels of a typical composite wingbox section and detecting faults in location and length. The work was performed during typical ground static tests by using a fibre optical sensing network embedded within relevant adhesive paste layers during the manufacturing process of the structure. In the reported investigation, the SHM system assumed the function of an NDI system tool. The results show that the developed SHM system has good reliability for the detection of both the position and size of damage areas that were artificially inserted within the test article during the bonding phase, showing its potential as a candidate to be used as a tool to verify the conditions of a bonded joint, as required by aviation authorities’ regulations.

A study on rocker arm defect inspection based on vision system

The rocker arm plays a crucial role in an internal combustion engine’s valve train system. Issues often arise when there are errors in the production of the rocker arm, such as the following: (1) misalignment of the rocker arm spindle hole and thread hole; and (2) bevel errors in the rocker arm spindle hole. This paper focuses on the development of a rocker arm chamfering and coaxial defect inspection system, which utilizes the RANSAC algorithm. The algorithm consists of five steps: (i) representing a 2D point cloud as a matrix with X and Y coordinates; (ii) detecting the number of circles present in the input point cloud; (iii) setting a tolerance threshold for the distance between the selected circle and other points, crucial for achieving high accuracy; (iv) specifying the maximum number of iterations to find the best model from the input points; (v) defining the maximum number of points that can belong to the same circle, considering point density and the anticipated circle’s surface. The real-time inspection system is designed with a camera mounted on top of the box, and two LED arrays positioned around the box at suitable distances. Test results using a collected data set demonstrate the effectiveness and accuracy of the proposed method in detecting each type of error. In the experiments, the proposed method successfully detected all coaxial defect samples and achieved a detection rate of 95.1% for chamfer defect samples.

Numerical modelling of bridge deck reinforcement corrosion based on analysis of GPR data

AbstractThis study explores the impact of corrosion on Ground Penetrating Radar (GPR) responses through practical experiments and numerical modelling, focusing on rebar diameter reduction, corrosion product layer thickness, crack formation and corrosion product filling in vertical and transverse crack. Practical experiments involved GPR testing of reinforced concrete slab. By analyzing B-scans we identify areas with moderate and severe corrosion. Numerical modelling using the Finite Difference Time Domain (FDTD) Method to model GPR signal propagation in a concrete bridge deck with corrosion is applied. Key finding includes a significant 26.70% increase in reflected wave amplitude when corrosion product filling in vertical crack increased by 400%, highlighting its extensive effect on signal GPR propagation. Reduced rebar diameter led to a 9.79% amplitude decrease and a 0.06 ns arrival time delay. Increased corrosion product layer thickness primarily affected arrival time with a 0.06 ns extension but significantly amplified GPR signal amplitude. These findings offer insights for improving GPR based corrosion detection and assessment methods, leading to more robust systems for concrete bridge deck inspection and maintenance. This paper contributes to understanding how corrosion affects the signal that is detected by GPR. This information can be used to improve the way that we manage and assess corrosion in concrete bridge deck.

Path planning of substation inspection robot based on high-precision positioning and navigation technology

Abstract Outdoor substation is an important part of power system. Substation inspection robot based on intelligent autonomous inspection system has become the research focus of substation unmanned inspection. In order to improve the positioning accuracy and speed of the system, a high-precision positioning algorithm of transformer detection robot is proposed in this paper. Tikhonov regularization is used to correct the pathological problem of the localization algorithm model. The observation amount of the receiver is increased by using four signals of a single base station with double frequency and double antenna, and the position is solved by using single difference carrier phase observation and the integer ambiguity is fixed. The input–output mapping of the neural network is designed according to the information acquisition and two-wheel angular velocity control of the detection robot. Using the hyperbolic tangent function as the activation function of MLP neural network, the MLP neural network with 32 neurons in each of the three hidden layers is determined. By optimizing reinforcement learning reward function, adding scoring rules, and reward parameters, this paper carries out the following simulation exploration work. The high-precision positioning algorithm of transformer inspection robot is compared with the existing algorithm, and the superiority of the algorithm is verified. The basic motion ability of the robot installed with the system was tested.

A Novel Vision- and Radar-Based Line Tracking Assistance System for Drone Transmission Line Inspection

This paper introduces a position controller for drone transmission line inspection (TLI) utilizing the integral sliding mode control (SMC) method. The controller, leveraging GNSS and visual deviation data, exhibits high accuracy and robust anti-interference capabilities. A deviation correction strategy is proposed to capture high-voltage transmission line information more robustly and accurately. Lateral position deviation is calculated using microwave radar data, attitude angle data, and deviation pixels derived from transmission line recognition via MobileNetV3. This approach enables accurate and stable tracking of transmission lines in diverse and complex environments. The proposed inspection scheme is validated in settings with 10-kilovolt and 110-kilovolt transmission lines using a drone with a diagonal wheelbase of 0.275 m. The experimental process is available in the YouTube link provided. The validation results affirm the effectiveness and feasibility of the proposed scheme. Notably, the absence of a high-precision positioning system in the validation platform highlights the scheme’s versatility, indicating applicability to various outdoor visual-based tracking scenarios using drones.

Numerical Simulation of an Online Cotton Lint Sampling Device Using Coupled CFD–DEM Analysis

Cotton processing is the process of converting harvested seed cotton into lint by cleaning, ginning, and cleaning the lint. The real-time acquisition of lint parameters during processing is critical in improving cotton processing quality and efficiency. The existing online inspection system cannot realize quantitative sampling detection, resulting in large fluctuations in the detection of moisture rate, and the impurity content of lint can only be measured according to the number of impurity grains and the percentage of impurity areas. This research developed a quantitative sampling device for cotton lint processing that can collect the right number of cotton samples and obtain the weight of the samples, laying the foundation for the accurate detection of cotton lint dampness and impurity rates. This research aimed to develop an online quantitative sampling device with a sampling plate as its core. The quantitative sampling procedure, consisting of a gas–solid two-phase flow in a cotton pipeline, was numerically simulated and experimentally analyzed using computational fluid dynamics (CFD) and the discrete element method (DEM). According to the coupling results, the maximum pressure differential between the top and bottom regions of the sampling plate when conveying was 1024.45 Pa. This pressure is adequate to allow for cotton samples to accumulate on the sampling plate. Simultaneously, the steady conveying speed of lint is 59.31% of the unloaded conveying wind speed, providing a theoretical foundation for the sampling time of the quantitative sample device in the processing chain. The results from testing the prototype indicate that the quantitative sampling device in the cotton flow can effectively perform the quantitative sampling of cotton lint under uniform conditions, with a sampling pass rate of 84%.

Will the central land inspection affect the intensity of local environmental protection? Evidence from China

Observing the impact and alienation of local government implementation of central policies is helpful for a comprehensive understanding of the effectiveness of central policy execution and the systematic formulation of central-local management policies. This paper takes the influence of land inspections on local government environmental behavior as the research object, based on the land inspection system initiated by the central government in 2006. It uses panel data from 30 provinces in China and a difference-in-differences method to assess the environmental protection crowding-out effect of land inspections for the first time and conducts an in-depth exploration of its mechanisms and heterogeneity. The study findings are as follows: (1) In a long-term sample spanning from 1997 to 2020, the establishment of land inspection bureaus did not significantly increase the level of environmental pollution in the host regions. (2) In a short-term sample spanning from 2000 to 2015, the establishment of land inspection bureaus significantly increased the level of environmental pollution in the host regions. (3) The environmental protection crowding-out effect of land inspections is mainly due to the restraint on local government fiscal revenue. (4) The larger the economic scale, the more significant the positive effect of land inspections on the level of environmental pollution in the host regions.