2,235 research outputs found

    Analysing uncertainty contributions in dimensional measurements of large-size objects by ultrasound sensors

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    According to the ever-increasing interest in metrological systems for dimensional measurements of large-size objects in a wide range of industrial sectors, several solutions based on different technologies, working principles, architectures and functionalities have been recently designed. Among these, a distributed flexible system based on a network of low-cost ultrasound (US) sensors - the Mobile Spatial coordinate Measuring System (MScMS) - has been developed. This article presents a possible approach to assess the system uncertainty referring to the measured point coordinates in the 3D space, focusing on the sources of measurement uncertainty and the related propagation la

    Design and development of a new large-scale metrology system: MScMS (Mobile Spatial coordinate Measuring System)

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    This thesis arises from the research activity developed at the Industrial Metrology and Quality Engineering Laboratory of DISPEA - Politecnico di Torino, on a new system prototype for dimensional measurement, called Mobile Spatial coordinate Measuring System (MScMS). MScMS determines dimensional features of large-size objects and has been designed to overcome some limits shown by other widespread measuring sets used nowadays, like Coordinate Measuring Machines (CMMs), theodolites/tacheometers, photogrammetry equipments, GPS based systems, Laser Trackers. Basing on a distributed sensor networks structure, MScMS can accomplish rapid dimensional measurements, in a wide range of indoor operating environments. It consists of distributed wireless devices, communicating with each other through radiofrequency (RF) and ultrasound (US) transceivers. This frame makes the system easy to handle and to move, and gives the possibility of placing its components freely around the workpiece. The wireless devices − known as "Crickets" − are developed by the Massachusetts Institute of Technology (MIT). Being quite small, light and potentially cheap (if mass produced), they fit to obtain a wide range of different network configurations. These features make the new system suitable for particular types of measurement, which can not be carried out, for example, by conventional CMMs. Typical is the case of large-size objects which are unable to be transferred to the measuring system area (because of their dimensions or other logistical constraints) and thus require the measuring system to be moved to them. In the dissertation the system is described exhaustively and characterized through practical experiments. Then, the system is compared to classical CMMs and the indoor-GPS (iGPS), an innovative laser based system for large-scale metrology. Finally, future directions of this research are give

    Roadmap on signal processing for next generation measurement systems

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    Signal processing is a fundamental component of almost any sensor-enabled system, with a wide range of applications across different scientific disciplines. Time series data, images, and video sequences comprise representative forms of signals that can be enhanced and analysed for information extraction and quantification. The recent advances in artificial intelligence and machine learning are shifting the research attention towards intelligent, data-driven, signal processing. This roadmap presents a critical overview of the state-of-the-art methods and applications aiming to highlight future challenges and research opportunities towards next generation measurement systems. It covers a broad spectrum of topics ranging from basic to industrial research, organized in concise thematic sections that reflect the trends and the impacts of current and future developments per research field. Furthermore, it offers guidance to researchers and funding agencies in identifying new prospects.AerodynamicsMicrowave Sensing, Signals & System

    The Mobile Spatial coordinate Measuring System II (MScMS-II):system description and preliminary assessmentof the measurement uncertainty

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    According to the increasing interest in metrological systems for the dimensional measurements of large-size objects in a wide range of industrial sectors, several solutions based on different technologies, working principles, architectures, and functionalities have recently been developed. Among all, the most flexible and easily transportable solutions are those that have aroused most interest and have found greater success. In order to address the needs of Large-Scale Metrology (LSM) applications, a distributed flexible system based on a network of low-cost InfraRed (IR) sensors – the Mobile Spatial coordinate Measuring System II (MScMS-II) – has been developed at the Industrial Quality and Metrology Laboratory of Politecnico di Torino. This paper presents a preliminary uncertainty assessment of the system referring to the measured point coordinates in the 3D space, focusing on the sources of measurement uncertainty and the related propagation laws. A preliminary metrological characterization of MScMS-II architecture, experimentally evaluated through a system prototype, is also presented and discussed

    Robust Localization from Incomplete Local Information

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    We consider the problem of localizing wireless devices in an ad-hoc network embedded in a d-dimensional Euclidean space. Obtaining a good estimation of where wireless devices are located is crucial in wireless network applications including environment monitoring, geographic routing and topology control. When the positions of the devices are unknown and only local distance information is given, we need to infer the positions from these local distance measurements. This problem is particularly challenging when we only have access to measurements that have limited accuracy and are incomplete. We consider the extreme case of this limitation on the available information, namely only the connectivity information is available, i.e., we only know whether a pair of nodes is within a fixed detection range of each other or not, and no information is known about how far apart they are. Further, to account for detection failures, we assume that even if a pair of devices is within the detection range, it fails to detect the presence of one another with some probability and this probability of failure depends on how far apart those devices are. Given this limited information, we investigate the performance of a centralized positioning algorithm MDS-MAP introduced by Shang et al., and a distributed positioning algorithm, introduced by Savarese et al., called HOP-TERRAIN. In particular, for a network consisting of n devices positioned randomly, we provide a bound on the resulting error for both algorithms. We show that the error is bounded, decreasing at a rate that is proportional to R/Rc, where Rc is the critical detection range when the resulting random network starts to be connected, and R is the detection range of each device.Comment: 40 pages, 13 figure

    Konvoluutioneuroverkot ja Gaussiset prosessit sensoridatan analysoimiseen

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    Different sensors are constantly collecting information about us and our surroundings, such as pollution levels or heart rates. This results in long sequences of noisy time series observations, often also referred to as signals. This thesis develops machine learning methods for analysing such sensor data. The motivation behind the work is based on three real-world applications. In one, the goal is to improve Wi-Fi networks and recognise devices causing interference from spectral data measured by a spectrum analyser. The second one uses ultrasound signals propagated through different paths to localise objects inside closed containers, such as fouling inside of industrial pipelines. In third, the goal is to model an engine of a car and its emissions. Machine learning builds models of complex systems based on a set of observations. We develop models that are designed for analysing time series data, and we build on existing work on two different models: convolutional neural networks (CNNs) and Gaussian processes (GPs). We show that CNNs are able to automatically recognise useful patterns both in 1D and 2D signal data, even when we use a chaotic cavity to scatter waves randomly in order to increase the acoustic aperture. We show how GPs can be used when the observations can be interpreted as integrals over some region, and how we can introduce a non-negativity constraint in such cases. We also show how Gaussian process state space models can be used to learn long- and short-term effects simultaneously by training the model with different resolutions of the data. The amount of data in our case studies is limited as the datasets have been collected manually using a limited amount of sensors. This adds additional challenges to modeling, and we have used different approaches to cope with limited data. GPs as a model are well suited for small data as they are able to naturally model uncertainties. We also show how a dataset can be collected so that it contains as much information as possible with the limited resources available in cases where we use GPs with integral observations. CNNs in general require large datasets, but we show how we can augment labeled data with unlabeled data by taking advantage of the continuity in sensor data.Erilaiset sensorit keräävät jatkuvasti dataa meistä ja ympäristöstämme, kuten ilmanlaadusta tai ihmisen sykkeestä. Tuloksena on pitkiä aikasarjahavaintoja, joita usein kutsutaan myös signaaleiksi. Tässä työssä kehitetään koneoppimismenetelmiä sensoridatan analysoimiseen. Motivaationa työssä on kolme erilaista käytännön sovellusta. Ensimmäisessä pyritään parantamaan Wi-Fi -verkkojen toimintaa tunnistamalla häiriötä aiheuttavia laitteita spektridatasta. Toisessa käytetään ultraääntä paikallistamaan kohteita suljettujen säiliöden sisällä. Kolmannessa mallinnetaan auton moottoria ja sen päästöjä. Koneoppiminen muodostaa malleja monimutkaisista järjestelmistä havaintojen pohjalta. Tässä työssä kehitetään malleja, jotka sopivat erityisesti aikasarjojen analysointiin. Nämä mallit perustuvat kahteen erilaiseen malliperheeseen: konvoluutioneuroverkkoihin ja Gaussisiin prosesseihin. Työssä kehitetään konvoluutioneuroverkkoja sekä yksi- että kaksiulotteisen signaalidatan analysointiin ja lisäksi osoitetaan, että niiden avulla voidaan tulkita myös signaaleja jotka on hajautettu satunnaisesti mittausalueen kasvattamiseksi. Työssä kehitetään Gaussisia prosesseja tapauksiin, joissa havainnot ovat integraaleja tuntemattoman funktion yli ja yleistetään menetelmä myös tilanteisiin joissa tuntemattoman funktion arvot ovat rajoitettuja, esimerkiksi ei-negativisia. Lisäksi esittelemme tavan, jolla gaussisia prosesseja hyödyntävät tila-avaruusmallit pystyvät oppimaan sekä pitkän että lyhyen aikavälin ilmiöitä käyttämällä opettamiseen datan eri resoluutioita. Työssä käsiteltävissä sovelluksissa datan määrä on verrattain pieni, sillä data on kerätty manuaalisesti vain pienellä määrällä sensoreita. Tässä työssä esitellään myös ratkaisuja pieniin datamääriin liittyviin haasteisiin. Näytämme, miten data voidaan kerätä niin, että se sisältää mahdollisimman paljon informaatiota pienistä resursseista huolimatta, tapauksissa, joissa havainnot vastaavat integraaleja alueiden yli. Konvoluutioneuroverkot tyypillisesti tarvitsevat opettamiseen paljon dataa, mutta työ esittelee miten opettamisessa voidaan täydentää luokiteltua dataa luokittelemattomalla datalla hyödyntämällä sensoridatan aikajatkuvuutta

    Multi-scale metrology for automated non-destructive testing systems

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    This thesis was previously held under moratorium from 5/05/2020 to 5/05/2022The use of lightweight composite structures in the aerospace industry is now commonplace. Unlike conventional materials, these parts can be moulded into complex aerodynamic shapes, which are diffcult to inspect rapidly using conventional Non-Destructive Testing (NDT) techniques. Industrial robots provide a means of automating the inspection process due to their high dexterity and improved path planning methods. This thesis concerns using industrial robots as a method for assessing the quality of components with complex geometries. The focus of the investigations in this thesis is on improving the overall system performance through the use of concepts from the field of metrology, specifically calibration and traceability. The use of computer vision is investigated as a way to increase automation levels by identifying a component's type and approximate position through comparison with CAD models. The challenges identified through this research include developing novel calibration techniques for optimising sensor integration, verifying system performance using laser trackers, and improving automation levels through optical sensing. The developed calibration techniques are evaluated experimentally using standard reference samples. A 70% increase in absolute accuracy was achieved in comparison to manual calibration techniques. Inspections were improved as verified by a 30% improvement in ultrasonic signal response. A new approach to automatically identify and estimate the pose of a component was developed specifically for automated NDT applications. The method uses 2D and 3D camera measurements along with CAD models to extract and match shape information. It was found that optical large volume measurements could provide suffciently high accuracy measurements to allow ultrasonic alignment methods to work, establishing a multi-scale metrology approach to increasing automation levels. A classification framework based on shape outlines extracted from images was shown to provide over 88% accuracy on a limited number of samples.The use of lightweight composite structures in the aerospace industry is now commonplace. Unlike conventional materials, these parts can be moulded into complex aerodynamic shapes, which are diffcult to inspect rapidly using conventional Non-Destructive Testing (NDT) techniques. Industrial robots provide a means of automating the inspection process due to their high dexterity and improved path planning methods. This thesis concerns using industrial robots as a method for assessing the quality of components with complex geometries. The focus of the investigations in this thesis is on improving the overall system performance through the use of concepts from the field of metrology, specifically calibration and traceability. The use of computer vision is investigated as a way to increase automation levels by identifying a component's type and approximate position through comparison with CAD models. The challenges identified through this research include developing novel calibration techniques for optimising sensor integration, verifying system performance using laser trackers, and improving automation levels through optical sensing. The developed calibration techniques are evaluated experimentally using standard reference samples. A 70% increase in absolute accuracy was achieved in comparison to manual calibration techniques. Inspections were improved as verified by a 30% improvement in ultrasonic signal response. A new approach to automatically identify and estimate the pose of a component was developed specifically for automated NDT applications. The method uses 2D and 3D camera measurements along with CAD models to extract and match shape information. It was found that optical large volume measurements could provide suffciently high accuracy measurements to allow ultrasonic alignment methods to work, establishing a multi-scale metrology approach to increasing automation levels. A classification framework based on shape outlines extracted from images was shown to provide over 88% accuracy on a limited number of samples

    Ultrasound-based density determination via buffer rod techniques: a review

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    Selected Papers from the 9th World Congress on Industrial Process Tomography

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    Industrial process tomography (IPT) is becoming an important tool for Industry 4.0. It consists of multidimensional sensor technologies and methods that aim to provide unparalleled internal information on industrial processes used in many sectors. This book showcases a selection of papers at the forefront of the latest developments in such technologies
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