Damage identification in structural health monitoring: a brief review from its implementation to the Use of data-driven applications

The damage identification process provides relevant information about the current state of a structure under inspection, and it can be approached from two different points of view. The first approach uses data-driven algorithms, which are usually associated with the collection of data using sensors. Data are subsequently processed and analyzed. The second approach uses models to analyze information about the structure. In the latter case, the overall performance of the approach is associated with the accuracy of the model and the information that is used to define it. Although both approaches are widely used, data-driven algorithms are preferred in most cases because they afford the ability to analyze data acquired from sensors and to provide a real-time solution for decision making; however, these approaches involve high-performance processors due to the high computational cost. As a contribution to the researchers working with data-driven algorithms and applications, this work presents a brief review of data-driven algorithms for damage identification in structural health-monitoring applications. This review covers damage detection, localization, classification, extension, and prognosis, as well as the development of smart structures. The literature is systematically reviewed according to the natural steps of a structural health-monitoring system. This review also includes information on the types of sensors used as well as on the development of data-driven algorithms for damage identification.Peer ReviewedPostprint (published version

Micro Fourier Transform Profilometry (μ\muFTP): 3D shape measurement at 10,000 frames per second

Recent advances in imaging sensors and digital light projection technology have facilitated a rapid progress in 3D optical sensing, enabling 3D surfaces of complex-shaped objects to be captured with improved resolution and accuracy. However, due to the large number of projection patterns required for phase recovery and disambiguation, the maximum fame rates of current 3D shape measurement techniques are still limited to the range of hundreds of frames per second (fps). Here, we demonstrate a new 3D dynamic imaging technique, Micro Fourier Transform Profilometry ($\mu$FTP), which can capture 3D surfaces of transient events at up to 10,000 fps based on our newly developed high-speed fringe projection system. Compared with existing techniques, $\mu$FTP has the prominent advantage of recovering an accurate, unambiguous, and dense 3D point cloud with only two projected patterns. Furthermore, the phase information is encoded within a single high-frequency fringe image, thereby allowing motion-artifact-free reconstruction of transient events with temporal resolution of 50 microseconds. To show $\mu$FTP's broad utility, we use it to reconstruct 3D videos of 4 transient scenes: vibrating cantilevers, rotating fan blades, bullet fired from a toy gun, and balloon's explosion triggered by a flying dart, which were previously difficult or even unable to be captured with conventional approaches.Comment: This manuscript was originally submitted on 30th January 1

Applying Augmented Reality to Outdoors Industrial Use

Augmented Reality (AR) is currently gaining popularity in multiple different fields. However, the technology for AR still requires development in both hardware and software when considering industrial use. In order to create immersive AR applications, more accurate pose estimation techniques to define virtual camera location are required. The algorithms for pose estimation often require a lot of processing power, which makes robust pose estimation a difficult task when using mobile devices or designated AR tools. The difficulties are even larger in outdoor scenarios where the environment can vary a lot and is often unprepared for AR. This thesis aims to research different possibilities for creating AR applications for outdoor environments. Both hardware and software solutions are considered, but the focus is more on software. The majority of the thesis focuses on different visual pose estimation and tracking techniques for natural features. During the thesis, multiple different solutions were tested for outdoor AR. One commercial AR SDK was tested, and three different custom software solutions were developed for an Android tablet. The custom software solutions were an algorithm for combining data from magnetometer and a gyroscope, a natural feature tracker and a tracker based on panorama images. The tracker based on panorama images was implemented based on an existing scientific publication, and the presented tracker was further developed by integrating it to Unity 3D and adding a possibility for augmenting content. This thesis concludes that AR is very close to becoming a usable tool for professional use. The commercial solutions currently available are not yet ready for creating tools for professional use, but especially for different visualization tasks some custom solutions are capable of achieving a required robustness. The panorama tracker implemented in this thesis seems like a promising tool for robust pose estimation in unprepared outdoor environments.Lisätyn todellisuuden suosio on tällä hetkellä kasvamassa usealla eri alalla. Saatavilla olevat ohjelmistot sekä laitteet eivät vielä riitä lisätyn todellisuuden soveltamiseen ammattimaisessa käytössä. Erityisesti posen estimointi vaatii tarkempia menetelmiä, jotta immersiivisten lisätyn todellisuuden sovellusten kehittäminen olisi mahdollista. Posen estimointiin (laitteen asennon- sekä paikan arviointiin) käytetyt algoritmit ovat usein monimutkaisia, joten ne vaativat merkittävästi laskentatehoa. Laskentatehon vaatimukset ovat usein haasteellisia varsinkin mobiililaitteita sekä lisätyn todellisuuden laitteita käytettäessä. Lisäongelmia tuottaa myös ulkotilat, jossa ympäristö voi muuttua usein ja ympäristöä ei ole valmisteltu lisätyn todellisuuden sovelluksille. Diplomityön tarkoituksena on tutkia mahdollisuuksia lisätyn todellisuuden sovellusten kehittämiseen ulkotiloihin. Sekä laitteisto- että ohjelmistopohjaisia ratkaisuja käsitellään. Ohjelmistopohjaisia ratkaisuja käsitellään työssä laitteistopohjaisia ratkaisuja laajemmin. Suurin osa diplomityöstä keskittyy erilaisiin visuaalisiin posen estimointi tekniikoihin, jotka perustuvat kuvasta tunnistettujen luonnollisten piirteiden seurantaan. Työn aikana testattiin useita ratkaisuja ulkotiloihin soveltuvaan lisättyyn todellisuuteen. Yhtä kaupallista työkalua testattiin, jonka lisäksi toteutettiin kolme omaa sovellusta Android tableteille. Työn aikana kehitetyt sovellukset olivat yksinkertainen algoritmi gyroskoopin ja magnetometrin datan yhdistämiseen, luonnollisen piirteiden seuranta-algoritmi sekä panoraamakuvaan perustuva seuranta-algoritmi. Panoraamakuvaan perustuva seuranta-algoritmi on toteuteutettu toisen tieteellisen julkaisun pohjalta, ja algoritmia jatkokehitettiin integroimalla se Unity 3D:hen. Unity 3D-integrointi mahdollisti myös sisällön esittämisen lisätyn todellisuuden avulla. Työn lopputuloksena todetaan, että lisätyn todellisuuden teknologia on lähellä pistettä, jossa lisätyn todellisuuden työkaluja voitaisiin käyttää ammattimaisessa käytössä. Tällä hetkellä saatavilla olevat kaupalliset työkalut eivät vielä pääse ammattikäytön vaatimalle tasolle, mutta erityisesti visualisointitehtäviin soveltuvia ei-kaupallisia ratkaisuja on jo olemassa. Lisäksi työn aikana toteutetun panoraamakuviin perustuvan seuranta-algoritmin todetaan olevan lupaava työkalu posen estimointiin ulkotiloissa.Siirretty Doriast

Efficient and Accurate Segmentation of Defects in Industrial CT Scans

Industrial computed tomography (CT) is an elementary tool for the non-destructive inspection of cast light-metal or plastic parts. A comprehensive testing not only helps to ensure the stability and durability of a part, it also allows reducing the rejection rate by supporting the optimization of the casting process and to save material (and weight) by producing equivalent but more filigree structures. With a CT scan it is theoretically possible to locate any defect in the part under examination and to exactly determine its shape, which in turn helps to draw conclusions about its harmfulness. However, most of the time the data quality is not good enough to allow segmenting the defects with simple filter-based methods which directly operate on the gray-values—especially when the inspection is expanded to the entire production. In such in-line inspection scenarios the tight cycle times further limit the available time for the acquisition of the CT scan, which renders them noisy and prone to various artifacts. In recent years, dramatic advances in deep learning (and convolutional neural networks in particular) made even the reliable detection of small objects in cluttered scenes possible. These methods are a promising approach to quickly yield a reliable and accurate defect segmentation even in unfavorable CT scans. The huge drawback: a lot of precisely labeled training data is required, which is utterly challenging to obtain—particularly in the case of the detection of tiny defects in huge, highly artifact-afflicted, three-dimensional voxel data sets. Hence, a significant part of this work deals with the acquisition of precisely labeled training data. Firstly, we consider facilitating the manual labeling process: our experts annotate on high-quality CT scans with a high spatial resolution and a high contrast resolution and we then transfer these labels to an aligned ``normal'' CT scan of the same part, which holds all the challenging aspects we expect in production use. Nonetheless, due to the indecisiveness of the labeling experts about what to annotate as defective, the labels remain fuzzy. Thus, we additionally explore different approaches to generate artificial training data, for which a precise ground truth can be computed. We find an accurate labeling to be crucial for a proper training. We evaluate (i) domain randomization which simulates a super-set of reality with simple transformations, (ii) generative models which are trained to produce samples of the real-world data distribution, and (iii) realistic simulations which capture the essential aspects of real CT scans. Here, we develop a fully automated simulation pipeline which provides us with an arbitrary amount of precisely labeled training data. First, we procedurally generate virtual cast parts in which we place reasonable artificial casting defects. Then, we realistically simulate CT scans which include typical CT artifacts like scatter, noise, cupping, and ring artifacts. Finally, we compute a precise ground truth by determining for each voxel the overlap with the defect mesh. To determine whether our realistically simulated CT data is eligible to serve as training data for machine learning methods, we compare the prediction performance of learning-based and non-learning-based defect recognition algorithms on the simulated data and on real CT scans. In an extensive evaluation, we compare our novel deep learning method to a baseline of image processing and traditional machine learning algorithms. This evaluation shows how much defect detection benefits from learning-based approaches. In particular, we compare (i) a filter-based anomaly detection method which finds defect indications by subtracting the original CT data from a generated ``defect-free'' version, (ii) a pixel-classification method which, based on densely extracted hand-designed features, lets a random forest decide about whether an image element is part of a defect or not, and (iii) a novel deep learning method which combines a U-Net-like encoder-decoder-pair of three-dimensional convolutions with an additional refinement step. The encoder-decoder-pair yields a high recall, which allows us to detect even very small defect instances. The refinement step yields a high precision by sorting out the false positive responses. We extensively evaluate these models on our realistically simulated CT scans as well as on real CT scans in terms of their probability of detection, which tells us at which probability a defect of a given size can be found in a CT scan of a given quality, and their intersection over union, which gives us information about how precise our segmentation mask is in general. While the learning-based methods clearly outperform the image processing method, the deep learning method in particular convinces by its inference speed and its prediction performance on challenging CT scans—as they, for example, occur in in-line scenarios. Finally, we further explore the possibilities and the limitations of the combination of our fully automated simulation pipeline and our deep learning model. With the deep learning method yielding reliable results for CT scans of low data quality, we examine by how much we can reduce the scan time while still maintaining proper segmentation results. Then, we take a look on the transferability of the promising results to CT scans of parts of different materials and different manufacturing techniques, including plastic injection molding, iron casting, additive manufacturing, and composed multi-material parts. Each of these tasks comes with its own challenges like an increased artifact-level or different types of defects which occasionally are hard to detect even for the human eye. We tackle these challenges by employing our simulation pipeline to produce virtual counterparts that capture the tricky aspects and fine-tuning the deep learning method on this additional training data. With that we can tailor our approach towards specific tasks, achieving reliable and robust segmentation results even for challenging data. Lastly, we examine if the deep learning method, based on our realistically simulated training data, can be trained to distinguish between different types of defects—the reason why we require a precise segmentation in the first place—and we examine if the deep learning method can detect out-of-distribution data where its predictions become less trustworthy, i.e. an uncertainty estimation

Optimization of star research algorithm for esmo star tracker

This paper explains in detail the design and the development of a software research star algorithm, embedded on a star tracker, by the ISAE/SUPAERO team. This research algorithm is inspired by musical techniques. This work will be carried out as part of the ESMO (European Student Moon Orbiter) project by different teams of students and professors from ISAE/SUPAERO (Institut Supe ́rieur de l’Ae ́ronautique et de l’Espace). Till today, the system engineering studies have been completed and the work that will be presented will concern the algorithmic and the embedded software development. The physical architecture of the sensor relies on APS 750 developed by the CIMI laboratory of ISAE/SUPAERO. First, a star research algorithm based on the image acquired in lost-in-space mode (one of the star tracker opera- tional modes) will be presented; it is inspired by techniques of musical recognition with the help of the correlation of digital signature (hash) with those stored in databases. The musical recognition principle is based on finger- printing, i.e. the extraction of points of interest in the studied signal. In the musical context, the signal spectrogram is used to identify these points. Applying this technique in image processing domain requires an equivalent tool to spectrogram. Those points of interest create a hash and are used to efficiently search within the database pre- viously sorted in order to be compared. The main goals of this research algorithm are to minimise the number of steps in the computations in order to deliver information at a higher frequency and to increase the computation robustness against the different possible disturbances

Using Machine-Learning to Optimize phase contrast in a Low-Cost Cellphone Microscope

Cellphones equipped with high-quality cameras and powerful CPUs as well as GPUs are widespread. This opens new prospects to use such existing computational and imaging resources to perform medical diagnosis in developing countries at a very low cost. Many relevant samples, like biological cells or waterborn parasites, are almost fully transparent. As they do not exhibit absorption, but alter the light's phase only, they are almost invisible in brightfield microscopy. Expensive equipment and procedures for microscopic contrasting or sample staining often are not available. By applying machine-learning techniques, such as a convolutional neural network (CNN), it is possible to learn a relationship between samples to be examined and its optimal light source shapes, in order to increase e.g. phase contrast, from a given dataset to enable real-time applications. For the experimental setup, we developed a 3D-printed smartphone microscope for less than 100 \$ using off-the-shelf components only such as a low-cost video projector. The fully automated system assures true Koehler illumination with an LCD as the condenser aperture and a reversed smartphone lens as the microscope objective. We show that the effect of a varied light source shape, using the pre-trained CNN, does not only improve the phase contrast, but also the impression of an improvement in optical resolution without adding any special optics, as demonstrated by measurements

Using Machine-Learning to Optimize phase contrast in a Low-Cost Cellphone Microscope

Cellphones equipped with high-quality cameras and powerful CPUs as well as GPUs are widespread. This opens new prospects to use such existing computational and imaging resources to perform medical diagnosis in developing countries at a very low cost. Many relevant samples, like biological cells or waterborn parasites, are almost fully transparent. As they do not exhibit absorption, but alter the light's phase only, they are almost invisible in brightfield microscopy. Expensive equipment and procedures for microscopic contrasting or sample staining often are not available. By applying machine-learning techniques, such as a convolutional neural network (CNN), it is possible to learn a relationship between samples to be examined and its optimal light source shapes, in order to increase e.g. phase contrast, from a given dataset to enable real-time applications. For the experimental setup, we developed a 3D-printed smartphone microscope for less than 100 \$ using off-the-shelf components only such as a low-cost video projector. The fully automated system assures true Koehler illumination with an LCD as the condenser aperture and a reversed smartphone lens as the microscope objective. We show that the effect of a varied light source shape, using the pre-trained CNN, does not only improve the phase contrast, but also the impression of an improvement in optical resolution without adding any special optics, as demonstrated by measurements

Detection, location and grasping objects using a stereo sensor on UAV in outdoor environments

The article presents a vision system for the autonomous grasping of objects with Unmanned Aerial Vehicles (UAVs) in real time. Giving UAVs the capability to manipulate objects vastly extends their applications, as they are capable of accessing places that are difficult to reach or even unreachable for human beings. This work is focused on the grasping of known objects based on feature models. The system runs in an on-board computer on a UAV equipped with a stereo camera and a robotic arm. The algorithm learns a feature-based model in an offline stage, then it is used online for detection of the targeted object and estimation of its position. This feature-based model was proved to be robust to both occlusions and the presence of outliers. The use of stereo cameras improves the learning stage, providing 3D information and helping to filter features in the online stage. An experimental system was derived using a rotary-wing UAV and a small manipulator for final proof of concept. The robotic arm is designed with three degrees of freedom and is lightweight due to payload limitations of the UAV. The system has been validated with different objects, both indoors and outdoor

Robust mobile robot localization based on a security laser: An industry case study

This paper aims to address a mobile robot localization system that avoids using a dedicated laser scanner, making it possible to reduce implementation costs and the robot's size. The system has enough precision and robustness to meet the requirements of industrial environments. Design/methodology/approach - Using an algorithm for artificial beacon detection combined with a Kalman Filter and an outlier rejection method, it was possible to enhance the precision and robustness of the overall localization system. Findings - Usually, industrial automatic guide vehicles feature two kinds of lasers: one for navigation placed on top of the robot and another for obstacle detection (security lasers). Recently, security lasers extended their output data with obstacle distance (contours) and reflectivity. These new features made it possible to develop a novel localization system based on a security laser. Research limitations/implications - Once the proposed methodology is completely validated, in the future, a scheme for global localization and failure detection should be addressed. Practical implications - This paper presents a comparison between the presented approach and a commercial localization system for industry. The proposed algorithms were tested in an industrial application under realistic working conditions. Social implications - The presented methodology represents a gain in the effective cost of the mobile robot platform, as it discards the need for a dedicated laser for localization purposes. Originality/value - This paper presents a novel approach that benefits from the presence of a security laser on mobile robots (mandatory sensor when considering industrial applications), using it simultaneously with other sensors, not only to guarantee safety conditions during operation but also to locate the robot in the environment. This paper is also valuable because of the comparison made with a commercialized system, as well as the tests conducted in real industrial environments, which prove that the approach presented is suitable for working under these demanding conditions.Project "TEC4Growth" - Pervasive Intelligence, Enhancers and Proofs of Concept with Industrial Impact/NORTE-01-0145-FEDER-000020" is fnanced by the North Portugal Regional Operational Programme (NORTE 2020), under the PORTUGAL 2020 Partnership Agreement, and through the European Regional Development Fund (ERDF).info:eu-repo/semantics/publishedVersio