Inspection Camera Research Articles

Investigating lubricant behavior in a partially flooded tapered roller bearing: Validation of a multiphase CFD solver for aerated oil sump via particle image velocimetry studies and high-speed camera acquisitions

In the recent years, the efficacy of utilizing sapphire outer rings as a viable tool for experimentally observing oil flows within rolling bearings has been demonstrated. However, in previous studies, such approach was applied under fully-flooded lubrication conditions exclusively. This paper presents the outcomes of observations and measurements conducted under oil-bath lubrication conditions of a vertical-axis Tapered Roller Bearing (TRB). The experimental work encompasses four operational speeds ranging from 1000 min−1 to 2500 min−1, and two oil levels, i.e. 10% and 100% of the bearing width. Pictures were captured using an High-Speed Camera (HSC) for a qualitative inspection. Particle Image Velocimetry (PIV) measurements were conducted for a quantitative assessment of the oil velocity fields. The findings reveal specific lubricant flows, highlight aeration, and show particular vorticity patterns that intensify with increasing speed. These experimentally obtained results align with Computational Fluid Dynamics (CFD) simulations conducted using a specially developed model in OpenFOAM®, leveraging a solver that accounts for aeration.

Method for evaluating and measuring cavity suitability for nesting wood ducks

AbstractThe challenges associated with climbing trees to measure cavity dimensions have limited the accumulation of knowledge regarding wood duck (Aix sponsa) nesting habitat and ecology. To overcome this issue, we developed a 2‐person method to measure external and internal tree‐cavity dimensions from the ground. Our approach uses a telescopic pole, wireless cavity inspection camera with a monitor, and reference scale, allowing an object of known length to be viewed and recorded inside the cavity. We tested our method using simulated cavities (n = 20), assessed accuracy by comparing the estimated and actual measurements, and evaluated precision between 2 observers. The average difference (±1 SE) between estimated and actual measurements (n = 40) for entrance width (0.9 ± 0.9 cm), entrance height (0.8 ± 1.1 cm), platform width (0.1 ± 3.7 cm), and platform length (1.0 ± 3.2 cm) were ≤1 cm. There was no significant difference between observer measurements for entrance width, entrance height, platform width, or platform length. Observers overestimated cavity depth by an average of 0.1 ± 1.6 cm and there was a significant difference (1.3 ± 2.2 cm) between observers for mean cavity depth. We applied the technique to naturally occurring cavities. The time to complete a natural‐cavity survey in the field (n = 37) averaged 12.2 ± 6.9 min. Our method increases the practicality, accessibility, and safety of researchers conducting cavity surveys for wood ducks and other cavity‐dependent wildlife through the use of a cost‐effective, cavity‐measuring tool.

Wireless flow-powered miniature robot capable of traversing tubular structures.

Wireless millimeter-scale robots capable of navigating through fluid-flowing tubular structures hold substantial potential for inspection, maintenance, or repair use in nuclear, industrial, and medical applications. However, prevalent reliance on external powering constrains these robots' operational range and applicable environments. Alternatives with onboard powering must trade off size, functionality, and operation duration. Here, we propose a wireless millimeter-scale wheeled robot capable of using environmental flows to power and actuate its long-distance locomotion through complex pipelines. The flow-powering module can convert flow energy into mechanical energy, achieving an impeller speed of up to 9595 revolutions per minute, accompanied by an output power density of 11.7 watts per cubic meter and an efficiency of 33.7%. A miniature gearbox module can further transmit the converted mechanical energy into the robot's locomotion system, allowing the robot to move against water flow at an average rate of up to 1.05 meters per second. The robot's motion status (moving against/with flow or pausing) can be switched using an external magnetic field or an onboard mechanical regulator, contingent on different proposed control designs. In addition, we designed kirigami-based soft wheels for adaptive locomotion. The robot can move against flows of various substances within pipes featuring complex geometries and diverse materials. Solely powered by flow, the robot can transport cylindrical payloads with a diameter of up to 55% of the pipe's diameter and carry devices such as an endoscopic camera for pipeline inspection, a wireless temperature sensor for environmental temperature monitoring, and a leak-stopper shell for infrastructure maintenance.

Pick and Place of Sensitive Chips with Vacuum-Free Gecomer® Tools

To cope the lack of suitable pick and place methods for sensitive chips, we present a new process using a gripper tool from the company INNOCISE called Gecomer®. The tool, made of polymer pillars, is using Van der Waals forces to create adhesion between the gripping tool and the chip surface. Hence, the tool works without any vacuum. While placing, the Van der Waals interactions are reduced by buckling those pillars resulting in a decreased contact area. The challenge is to find the right parameters to avoid a re-gripping of the chip during the Gecomer®-movement away from the chip. In this paper we present results on the placement accuracy including the reproducibility compared to a standard vacuum tool for different process parameters. For that, we used a pick and place machine with built-in inspection camera to examine the deviation from the target position. It is revealed that both vacuum and polymer gripper have a similar deviation of approx. ±15 μm. Hence, the placing accuracy is not negatively influenced by the Gecomer® tool and depends only on the machine parameters. Furthermore, we analyze the surfaces of the chips, whether there are any residues of the gripping tool. In a first optical inspection, no damages or residues could be found. The new vacuum-free pick and place process has the potential to open new opportunities in the assembly of sensitive (MEMS)- chips regarding reproducibility, accuracy, and process throughput.

Research on a multi-dimensional image information fusion algorithm based on NSCT transform

Traditional inspection cameras determine targets and detect defects by capturing images of their light intensity, but in complex environments, the accuracy of inspection may decrease. Information based on polarization of light can characterize various features of a material, such as the roughness, texture, and refractive index, thus improving classification and recognition of targets. This paper uses a method based on noise template threshold matching to denoise and preprocess polarized images. It also reports on design of an image fusion algorithm, based on NSCT transform, to fuse light intensity images and polarized images. The results show that the fused image improves both subjective and objective evaluation indicators, relative to the source image, and can better preserve edge information and help to improve the accuracy of target recognition. This study provides a reference for the comprehensive application of multi-dimensional optical information in power inspection.Graphical

Classification and sizing of surface defects of pipelines based on the results of complex diagnostics by ultrasonic, eddy current and visual and measuring methods of nondestructive testing

The issues of classification and determination of parameters of surface operational defects according to the results of ultrasonic, eddy current and visual and measuring methods of nondestructive testing are considered. At the same time, the visual and measuring method was realized with the use of a television inspection camera equipped with a computer vision function and a laser triangulation sensor. The paper presents a dataset containing 5760 images of pipelines with and without pitting corrosion. A convolutional neural network (CNN) is presented, which has been applied to classify the images obtained from a TV inspection camera into images without corrosion and images with pitting corrosion. The paper presents a dataset containing 269 measurements of planar and volumetric surface defects. A model for surface defect sizing based on gradient boosting is presented. The paper develops an algorithm for classification and sizing of surface defects in complex diagnostics, in which the obtained models are applied, and determines the accuracy of this algorithm by the RMSE metric, which was calculated within the studying test data set and amounted to 0.011 mm.

Design and performance of a slender soft continuum manipulator for tall structure inspection

PurposeThe current methods for inspecting tall or deep structures such as towers, chimneys, silos, and wells suffer from certain constraints. Manual and assisted inspection methods including humans, drones, wall climbing robots, and others are either costly, have a limited operation time, or affected by field conditions, such as temperature and radiation. This study aims to overcome the presented challenges through a teleoperated soft continuum manipulator capable of inspecting tall or deep structures with high resolution, an unlimited operation time and the ability to use different arms of the manipulator for different environments and structure sizes.Design/methodology/approachThe teleoperated manipulator uses one rotary and two tendon actuators to reach and inspect the interior of a tall (or deep) structure. A sliding part along the manipulator’s body (arm constrainer and tendon router) induces a variable-length bending segment, allowing an inspection camera to be placed at different distances from the desired location.FindingsThe experiments confirmed the manipulator’s ability to inspect different locations in the structure’s interior. The manipulator also demonstrated a submillimeter motion resolution vertically and a 2.5 mm per step horizontally. The inspection time of the full structure was 48.53 min in the step-by-step mode and was calculated to be 4.23 min in the continuous mode.Originality/valueThe presented manipulator offers several design novelties: the arm’s thin-wide cross-section, the variable-length bending segment in a fixed-length body, the external rolling tendon routing and the ability to easily replace the arm with another of different material or dimensions to suite different structures and environments.

The influence of drought, precipitation and fossorial mammal reintroduction on the density of fossorial arthropods and their burrows in arid Australia

AbstractThe density and abundance of arid‐dwelling taxa often change significantly in response to precipitation fluctuations and the abundance of their predators. The survival and density of burrowing arthropods and their burrows in arid environments following prolonged dry periods and subsequent rains is poorly understood, as is the potential influence of reintroductions of their predators, such as fossorial mammals. The persistence of these arthropods and their burrows may be important for other species that rely on them for food or use their burrows for shelter. In this study, we examined the density of burrowing and ground‐nesting arthropods and their burrows in Australia's Strzelecki Desert over two years between 2019 and 2021. This period spanned the tail‐end of the worst drought on record and subsequent drought‐breaking rains. We employed a Before‐After Control‐Impact (BACI) study design to examine the short‐term effects of a fossorial mammal reintroduction of the greater bilby (Macrotis lagotis) into predator‐free fenced exclosures and used an inspection camera to detect the presence of spiders and other taxa within individually marked burrows. We observed the largest changes in arthropod abundance and burrow density between a period that encompassed a third consecutive summer in drought and the commencement of drought‐breaking rains, with some taxa declining by as much as 77% (p < 0.001). While the density of harvester ant middens erupted over this time, the density of tarantulas, trapdoor spiders and scorpions declined significantly. The greater bilby reintroduction had no short‐term effect on the densities of the arthropods or their burrows, but their arrival may have implications on their post‐drought recovery. Further studies are needed to determine if the significant declines in arthropod populations and burrows are reflective of normal boom‐bust population dynamics due to the poor natural history knowledge of the arthropods we examined.

A TRIZ-Supported Concept and Protocol Development for Roof Tile Transportation and Inspection Systems

Currently, the use of manual labour in the transportation and inspection systems of leading roof tile manufacturing companies in Indonesia is still prevalent. Manual labour is usually labour-intensive, has higher risks of musculoskeletal disorders, and produces frequent occurrences of errors and losses. Furthermore, the current studies of suitable concepts and test protocols for roof tile transportation at the manufacturing stage as well as their inspection systems are not practicable in Indonesia. There is also no study that has used the theory of inventive problem-solving (TRIZ) in the development of concepts and protocols for roof tile transportation and inspection systems. Using TRIZ as a supporting tool, this study investigated the development of a transportation system to be employed during the manufacturing of the roof tile and a test protocol for their usability in Indonesian companies to overcome this concern. The study included screening and scoring concepts and usability test protocols identified from the existing literature, with the support of TRIZ tools such as the engineering contradiction, contradiction matrix, and inventive principles. Thus, the finalised concept comprised a belt conveyor system (Inventive Principle 20: Continuity of Useful Action) with a flipping mechanism for transportation and a vision-based camera for inspection. Results of the study showed that the concept excelled in cost, durability, reliability, versatility, low risk to the product, efficiency, and safety. The t-test protocol (Inventive Principle 23: Feedback) was selected based on the results due to its versatility in testing efficiency, reliability, and productivity. It was concluded that this concept has the potential to alleviate roof tile workers of physical work and reduce the prevalence of musculoskeletal disorders.

Implementation of Non-Contact Temperature Distribution Monitoring Solutions for Railway Vehicles in a Sustainability Development System Transport.

The implementation of temperature sensors represented by thermal imaging cameras is becoming increasingly rational. It is playing an important role in the socio-economic environment, in industry, scientific-research work. The main objective of the work is to assess the quality of the railway vehicles in exploitation and their thermal insulation, localise thermal bridges, and the tightness of the body using the FLIR-E6390 thermal inspection camera. An integration of test methods (research methods) was used including a diagnostic method based on a thermographic study integrated with the system approach method and system failure mode effects analysis (SFMEA). The scientific-research work included studies of seven types of railway vehicles in exploitation. A number of conclusions were reached. Specifically providing implementation of innovative and non-contact temperature distribution monitoring solutions for railway vehicles in a sustainability development system transport. Demonstrated the disparities between the different types of vehicles. Next, the identification of critical elements of their thermal insulation, the location of thermal bridges, and the tightness of the body of the rail vehicles, particularly the doors and inter-unit connections. The study covered the state of consumption of stationary electricity (for non-traction needs), implementing innovative indicators for stationary electricity consumption of railway vehicles as a new approach.

Low-cost Colonoscopic Simulator and Colonoscope to Train Basic Skills in Colonoscopy during Undergraduate Studies

Abstract Objective To develop a low-cost simulator model and a colonoscope with materials that are easily accessible to offer training on colonoscopy skills during undergraduate studies. Since this is the procedure of choice for colorectal cancer screening, the general practitioner must be able to recognize its main indications, preparation, and complications. Methods Using materials such as a mannequin, a vehicle inspection camera, a conduit, polyvinyl chloride (PVC) pipe, acrylic, wood, and red paint, we built a simulator and a 150-cm long and 20-to-25-mm thick colonoscope. The colonoscope's handle and handhold were made of acrylic, the colonoscope's mobile end was made with articulated PVC rings, and the up and down movements were performed according to the traction of the steel cables. The camera attached to its distal end enables connection to a smartphone to view the image. In the simulator, the conduit was inserted into the mannequin to simulate the curvatures of the colon. Red spray paint was used to simulate the staining of the colonic mucosa in the inner region of the mannequin and the adventitial layer in the outer region. Results We were able to build a simulator and a colonoscope with a total amount of R$ 182.82 (roughly US$ 36.50). Both were tested and proved to be useful in the acquisition of psychomotor and cognitive skills in colonoscopy. Conclusion The simulator and colonoscope developed by us are cost-effective, useful in the acquisition of psychomotor and cognitive skills in colonoscopy, and can facilitate the structuring of a training program for undergraduate students.

A New Rail Surface Defects Detection Approach Using 3D Laser Cameras Based on ResNet50

Rail transportation systems, which are used as one of the most common means of transportation worldwide, should be regularly inspected to prevent accidents that may occur. The rail condition monitoring can be performed in high accuracy and real time using computer vision, deep learning algorithms today. In this study, a new deep learning based approach using 3D laser cameras for rail inspection is presented. In the proposed approach, two 3D laser cameras placed on a real train, seeing the rail line from the left and right surfaces were used. These data consisting of sensitive distance value constitute the input data of the ResNet50 transfer learning model. The training was carried out on Nvidia Cuda supported graphics processing units using ResNet50 Convolutional Neural Network. During the test phase, the operation speed and accuracy rate of the method was measured by repeating the process on real-time rail profiles. The accuracy rate was calculated as 94%. As a result a new approach is presented based on deep learning using 3D laser cameras for rail inspection is presented.

Pipe Line Inspection Robot

Abstract: Pipelines are mainly constructed to transport all kinds of fluids and gases. Many accidents had occurred from fluid leaks because of cracks and corrosion of pipelines. To eliminate or minimize the accidents periodical inspection of pipelines should be done. Analysis and control of autonomous robot for pipeline inspection requires design and model of a robot equipped with the proper sensors. This project deals with the design, modelling (software) and simulation of pipeline inspection robot. The robot is an autonomous mobile robot. The robot is placed at the entrance of the pipe, by giving suitable commands it will move forward and inspect the defects inside the pipe. This inspection robot includes camera for visual inspection to identify the cracks and corrosion in pipe. It captures the inner images of the pipe for further investigation. This robot also includes a LIDAR sensor for mapping of the pipe. The mapping and navigation are all done in ROS (Robot Operating System) with help of Gazebo and Rviz. The design of the robot is created in Fusion 360 and then it is transferred into Gazebo. The simulation of robot is done in circular pipes.

Contactless Material Tensile Testing Using a High-Resolution Camera

This article deals with the use of contactless measurement with a high-resolution imaging device during tensile testing of materials in a universal tearing machine (UTM). Setting the material parameters in tensile testing is based on changes in the geometrical properties of the sample being tested. In this article, authors propose the method and system for automated measuring the height, width, and crack occurrence during tensile testing. The system is also able to predict the location of crack occurrence. The proposed method is based on selected algorithms of image analysis, feature extraction, and template matching. Our video extensometry, working with common inspection cameras operating in visible range, can be an alternative method to expensive laser extensometry machines. The motivation of our work was to develop an automated measurement system for use in a UTM.

Image Segmentation of a Sewer Based on Deep Learning

An accurate assessment of the type and extent of sewer damage is an important prerequisite for maintenance and repair. At present, distinguishing drainage pipe defect types in the engineering field mainly relies on the human eye, which is time consuming, labor intensive, and subjective. Some studies have used deep learning to classify the types of pipe defects, but this method can only identify one main pipe defect. However, sometimes a combination of defects, such as corrosion and precipitation on a section of pipe wall, can be classified as one category by picture classification, which is significantly different from the reality. Furthermore, the deep learning method for defect classification is unable to pinpoint the precise location and severity of a defect or estimate the number of flaws and the cost of maintenance and repair. Therefore, an image segmentation method based on deep convolutional neural networks is proposed to achieve pixel-level image segmentation of defect regions while classifying pipe defects. Compared with the deep learning network for defect classification, it can segment a variety of defects and reduce the number of samples, which is convenient for defect measurement. First, the image defect locations of seven typical defects were manually labeled to create the dataset. Then, a model based on the SegNet network was used to label defect areas automatically in an image. The pipeline image dataset was used to test the previously trained model using the CamVid dataset. Finally, the model was applied to drainage pipe network images that were provided by periscope and closed-circuit television inspection cameras, and the pixel accuracy of image segmentation reached 80%. From the results, it can be concluded that image segmentation and annotation technology based on deep learning is applicable to sewer defect detection. The identification results of pipeline defects were accurate. The SegNet model is a reliable method for image analysis of pipeline defects, which can accurately evaluate the type and degree of sewer damage.

Passenger Surveillance Using Deep Learning in Post-COVID-19 Intelligent Transportation System.

Intelligent Transport System should be renovated in many aspects in post-pandemic situation like COVID-19. The passenger-count inside a car will be restricted based on the vehicle capacity and the COVID-19 hot-spot zone. Traffic rules will be impacted to align with a similar contagious outbreak. The on-road ‘Yellow-Vulture’ cameras need to incorporate such surveillance rules to monitor related anomalies for preventing contamination. To maintain safe-distance, an automatic surveillance system will be preferred by the Government very soon. Moreover, facial mask usage during the journey has become an essential habit to stop the spread of the infection. In this article, we have proposed a deep-Learning based framework that employs an augmented image data set to provide proper surveillance in the transport system to maintain the health protocols. Fast and accurate detection of the number of passengers inside a car and their face masks from the traffic inspection camera feed has been demonstrated. We have exploited the advantages of the popular Transfer Learning approach with novel variations of images while performing the training. To the best of our knowledge, this is the first attempt to watch over in-vehicle social-distancing in post-pandemic circumstances through deep-Learning based image analysis. The superiority of the proposed framework has been established over several state-of-the-art techniques using different numerical metrics and visual comparisons along with a support of statistical hypothesis test. Our technique has achieved 98.5% testing accuracy in various adverse conditions. Zero-shot evaluation has been explored for the Real-Time-Medical-Mask-Detection data set Wang et al. (Real-Time-Medical-Mask-Detection, 2020a https://github.com/TheSSJ2612/Real-Time-Medical-Mask-Detection/, Accessed 14 Nov 2020), where we have attained 96.4% accuracy that manifests the generalization of the network.

Warehouse Inspection with an Autonomous Micro Air Vehicle

In this work, we present an approach to address the problem of warehouse inspection using a MicroAir Vehicle (MAV) that performs autonomous flight by following a set of waypoints in a GPS-denied environment. During the navigation, a second onboard camera is used to observe the inspection area where it is expected to observe QR codes attached to packages placed on shelves. To this end, we use a metric monocular simultaneous localization And Mapping (SLAM) system to estimate the MAVs position in metres, avoiding the need for an external positioning system. The onboard computer executes the detection and decoding of QR codes and the SLAM system. The MAV can also communicate with a Ground Control Station (GCS) to display telemetry, the MAVs position, images from the inspection camera with detected QR codes, and a list of the found packages. This approach was used to compete in the indoors competition of the International Micro Air Vehicle Competition (IMAV) 2019, where we received the special award: Best Flight Performance.

Automating Aircraft Scanning for Inspection or 3D Model Creation with a UAV and Optimal Path Planning

Visual inspections of aircraft exterior surfaces are required in aircraft maintenance routines for identifying possible defects such as dents, cracks, leaking, broken or missing parts, etc. This process is time-consuming and is also prone to error if performed manually. Therefore, it has become a trend to use mobile robots equipped with visual sensors to perform automated inspections. For such a robotic inspection, a digital model of the aircraft is usually required for planning the robot’s path, but a CAD model of the entire aircraft is usually inaccessible to most maintenance shops. It is very labor-intensive and time-consuming to generate an accurate digital model of an aircraft, or even a large portion of it, because the scanning work still must be performed manually or by a manually controlled robotic system. This paper presents a two-stage approach of automating aircraft scanning with an unmanned aerial vehicle (UAV) or autonomous drone equipped with a red–green–blue and depth (RGB-D) camera for detailed inspection or for reconstructing a digital replica of the aircraft when its original CAD model is unavailable. In the first stage, the UAV–camera system follows a predefined path far from the aircraft surface (for safety) to quickly scan the aircraft and generate a coarse model of the aircraft. Then, an optimal scanning path (much closer to the surface) in the sense of the shortest flying distance for full coverage is computed based on the coarse model. In the second stage, the UAV–camera system follows the computed path to closely inspect the surface for possible defects or scan the surface for generating a dense and precise model of the aircraft. We solved the coverage path planning (CPP) problem for the aircraft inspection or scanning using a Monte Carlo tree search (MCTS) algorithm. We also implemented the max–min ant system (MMAS) strategy to demonstrate the effectiveness of our approach. We carried out a digital experiment and the results showed that our approach can scan 70% of the aircraft surface within one hour, which is much more efficient than manual scanning.

IApp: An Autonomous Inspection, Auscultation, Percussion, and Palpation Platform.

Disease symptoms often contain features that are not routinely recognized by patients but can be identified through indirect inspection or diagnosis by medical professionals. Telemedicine requires sufficient information for aiding doctors' diagnosis, and it has been primarily achieved by clinical decision support systems (CDSSs) utilizing visual information. However, additional medical diagnostic tools are needed for improving CDSSs. Moreover, since the COVID-19 pandemic, telemedicine has garnered increasing attention, and basic diagnostic tools (e.g., classical examination) have become the most important components of a comprehensive framework. This study proposes a conceptual system, iApp, that can collect and analyze quantified data based on an automatically performed inspection, auscultation, percussion, and palpation. The proposed iApp system consists of an auscultation sensor, camera for inspection, and custom-built hardware for automatic percussion and palpation. Experiments were designed to categorize the eight abdominal divisions of healthy subjects based on the system multi-modal data. A deep multi-modal learning model, yielding a single prediction from multi-modal inputs, was designed for learning distinctive features in eight abdominal divisions. The model's performance was evaluated in terms of the classification accuracy, sensitivity, positive predictive value, and F-measure, using epoch-wise and subject-wise methods. The results demonstrate that the iApp system can successfully categorize abdominal divisions, with the test accuracy of 89.46%. Through an automatic examination of the iApp system, this proof-of-concept study demonstrates a sophisticated classification by extracting distinct features of different abdominal divisions where different organs are located. In the future, we intend to capture the distinct features between normal and abnormal tissues while securing patient data and demonstrate the feasibility of a fully telediagnostic system that can support abnormality diagnosis.

Survey on Artificial Intelligence Based Autonomous Inspection Systems for an Industry 4.0

The main idea of this project is to inspect the manufactured component through machine vision system .Through this project mainly the dimension correctness of the manufactured component is tested .The aim of the project is to improve the quality of the product .The various parts of the automatic inspection machine includes conveyors ,test table ,inspection camera, rejection bin ,image processing unit, monitoring system .Firstly, the specimen to be inspected is taken to the rotating test table by means of a conveyors .Then the inspection camera‟s detect the specimen under study on the rotating test table and the image of the specimen is captured by the camera and taken for further processing .The image is processed in terms of pixels by the image processing system. The generated image of the specimen is compared with the standard specimen by the image processing system and the result of inspection is projected on the monitoring system. The rejected specimen is automatically taken to the rejection bin by means of nozzle systems. This process is again repeated for every specimen. The overall setup of the machine is bridge type so it could be possible to inspect more than one work piece at a time.