Computer Vision Based Structural Identification Framework for Bridge Health Mornitoring

The objective of this dissertation is to develop a comprehensive Structural Identification (St-Id) framework with damage for bridge type structures by using cameras and computer vision technologies. The traditional St-Id frameworks rely on using conventional sensors. In this study, the collected input and output data employed in the St-Id system are acquired by series of vision-based measurements. The following novelties are proposed, developed and demonstrated in this project: a) vehicle load (input) modeling using computer vision, b) bridge response (output) using full non-contact approach using video/image processing, c) image-based structural identification using input-output measurements and new damage indicators. The input (loading) data due vehicles such as vehicle weights and vehicle locations on the bridges, are estimated by employing computer vision algorithms (detection, classification, and localization of objects) based on the video images of vehicles. Meanwhile, the output data as structural displacements are also obtained by defining and tracking image key-points of measurement locations. Subsequently, the input and output data sets are analyzed to construct novel types of damage indicators, named Unit Influence Surface (UIS). Finally, the new damage detection and localization framework is introduced that does not require a network of sensors, but much less number of sensors. The main research significance is the first time development of algorithms that transform the measured video images into a form that is highly damage-sensitive/change-sensitive for bridge assessment within the context of Structural Identification with input and output characterization. The study exploits the unique attributes of computer vision systems, where the signal is continuous in space. This requires new adaptations and transformations that can handle computer vision data/signals for structural engineering applications. This research will significantly advance current sensor-based structural health monitoring with computer-vision techniques, leading to practical applications for damage detection of complex structures with a novel approach. By using computer vision algorithms and cameras as special sensors for structural health monitoring, this study proposes an advance approach in bridge monitoring through which certain type of data that could not be collected by conventional sensors such as vehicle loads and location, can be obtained practically and accurately

Pattern Recognition

A wealth of advanced pattern recognition algorithms are emerging from the interdiscipline between technologies of effective visual features and the human-brain cognition process. Effective visual features are made possible through the rapid developments in appropriate sensor equipments, novel filter designs, and viable information processing architectures. While the understanding of human-brain cognition process broadens the way in which the computer can perform pattern recognition tasks. The present book is intended to collect representative researches around the globe focusing on low-level vision, filter design, features and image descriptors, data mining and analysis, and biologically inspired algorithms. The 27 chapters coved in this book disclose recent advances and new ideas in promoting the techniques, technology and applications of pattern recognition

Zařízení pro diagnostiku valivých vedení

Disertační práce se zabývá vysoce aktuální problematikou, která se týká diagnostiky valivých vedení. Soustřeďuje se zejména na lineární valivá vedení, která jsou základními konstrukčními prvky v dané oblasti. Disertační práce představuje vyvinuté originální patentované řešení diagnostiky lineárních valivých vedení, které je založené na měření vibrací. Řešení spočívá ve využití diagnostické části, která sdílí valivé elementy i vodicí profil s nosnou částí tělesa lineárního valivého vedení a která je integrována do jeho konstrukce. Tato diagnostická část je volná a nezatížená vnějšími silovými účinky, poškození lineárního valivého vedení se proto ve vibračním signálu projeví velmi intenzivně. Navržené diagnostické zařízení bylo ověřeno ve výrobním zařízení svařovací linky v automobilovém průmyslu.The thesis resolves the current issue of rolling guide diagnostics. It focuses mainly on linear rolling guides that create basic parts in that field. The thesis presents a developed original solution for diagnostics of linear rolling guide that has been protected by a European patent. The diagnostics employs a vibration measurement on a diagnostic part, which shares rolling elements and a guiding profile with a carriage of the linear rolling guide. The diagnostic part integrated into the carriage is free of external loads and therefore provides sensitive and reliable damage diagnostics of linear rolling guides. The developed diagnostic device has been verified under real operating conditions in the production line of the automotive industry.

Structural health monitoring meets data mining

With the development of sensing and data processing techniques, monitoring physical systems in the field with a sensor network is becoming a feasible option for many domains. Such monitoring systems are referred to as Structural Health Monitoring (SHM) systems. By definition, SHM is the process of implementing a damage detection and characterisation strategy for engineering structures, which involves data collection, damage-sensitive feature extraction and statistical analysis. Most of the SHM process can be addressed by techniques from the Data Mining domain, so I conduct this research by combining these two fields. The monitoring system employed in this research is a sensor network installed on a Dutch highway bridge, which aims to monitor dynamic health aspects of the bridge and its long-term degradation. I have explored the specific focus of each sensor type under multiple scales, and analysed the dependencies between sensor types. Based on landmarks and constraints, I have proposed a novel predefined pattern detection method to select traffic events for modal analysis. I have analysed the influence of temperature and traffic mass on natural frequencies, and verified that natural frequencies decrease with temperature increases, but the influence of traffic mass is weaker than that of temperature.Chinese CSC Dutch STWAlgorithms and the Foundations of Software technolog

Selected Papers from Experimental Stress Analysis 2020

This Special Issue consists of selected papers from the Experimental Stress Analysis 2020 conference. Experimental Stress Analysis 2020 was organized with the support of the Czech Society for Mechanics, Expert Group of Experimental Mechanics, and was, for this particular year, held online in 19–22 October 2020. The objectives of the conference included identification of current situation, sharing professional experience and knowledge, discussing new theoretical and practical findings, and the establishment and strengthening of relationships between universities, companies, and scientists from the field of experimental mechanics in mechanical and civil engineering. The topics of the conference were focused on experimental research on materials and structures subjected to mechanical, thermal–mechanical, and dynamic loading, including damage, fatigue, and fracture analyses. The selected papers deal with top-level contemporary phenomena, such as modern durable materials, numerical modeling and simulations, and innovative non-destructive materials’ testing

Dynamical systems : control and stability

Proceedings of the 13th Conference „Dynamical Systems - Theory and Applications" summarize 164 and the Springer Proceedings summarize 60 best papers of university teachers and students, researchers and engineers from whole the world. The papers were chosen by the International Scientific Committee from 315 papers submitted to the conference. The reader thus obtains an overview of the recent developments of dynamical systems and can study the most progressive tendencies in this field of science

Manufacturing Metrology

Metrology is the science of measurement, which can be divided into three overlapping activities: (1) the definition of units of measurement, (2) the realization of units of measurement, and (3) the traceability of measurement units. Manufacturing metrology originally implicates the measurement of components and inputs for a manufacturing process to assure they are within specification requirements. It can also be extended to indicate the performance measurement of manufacturing equipment. This Special Issue covers papers revealing novel measurement methodologies and instrumentations for manufacturing metrology from the conventional industry to the frontier of the advanced hi-tech industry. Twenty-five papers are included in this Special Issue. These published papers can be categorized into four main groups, as follows: Length measurement: covering new designs, from micro/nanogap measurement with laser triangulation sensors and laser interferometers to very-long-distance, newly developed mode-locked femtosecond lasers. Surface profile and form measurements: covering technologies with new confocal sensors and imagine sensors: in situ and on-machine measurements. Angle measurements: these include a new 2D precision level design, a review of angle measurement with mode-locked femtosecond lasers, and multi-axis machine tool squareness measurement. Other laboratory systems: these include a water cooling temperature control system and a computer-aided inspection framework for CMM performance evaluation

Desenvolvimento e validação de um método dinâmico, baseado em emissão acústica, para a caracterização em processo de rebolos convencionais

Tese (doutorado) - Universidade Federal de Santa Catarina, Centro Tecnológico, Programa de Pós-Graduação em Engenharia Mecânica, Florianópolis, 2015A tecnologia de emissão acústica (EA) é utilizada no desenvolvimento de um método dinâmico para caracterização em processo (DICM) da topografia de rebolos convencionais. Experimentos planejados são conduzidos em uma bancada de ensaios desenvolvida, contendo um software de aquisição de sinais de EA. A bancada de ensaios e o software de aquisição possibilitam o reconhecimento de interferências entre rebolo (vs= 30 m/s) e uma ponta de diamante, na faixa de deformação elástica das ferramentas. Os sinais de EARAW adquiridos de forma on-line e originados destas interferências são utilizados como dados de entrada para técnicas de processamento de sinal e para uma Rede Neural (RN). Ambas as análises são efetuadas fora do processo de retificação, representando um método dinâmico de caracterização pós-processo (DPCM) da topografia de rebolos. Os resultados do DPCM são validados através de medições específicas nas peças retificadas (p. ex., rugosidade, microscopia, camada termicamente afetada, desvio de forma) e em réplicas extraídas da topografia do rebolo. Com base nas técnicas de processamento de sinais validadas e propostas no DPCM,implementa-se o DICM. Para este método, desenvolve-se uma bancada experimental baseada na aquisição de sinais de múltiplos transdutores,na qual sinais de EA e de força são medidos. A bancada experimental eseu software de aquisição permitem a caracterização em processo da topografia de rebolos convencionais através da extração de informações quantitativas dos sinais on-line de EARAW adquiridos durante asinterferências entre rebolo (vs= 30 m/s) e ponta de diamante na faixa de 1 µm. A informação quantificada associada com a topografia do rebolo é baseada na análise em processo dos sinais de EARAW nos domínios dotempo e frequência. Os resultados de ambas as análises são obtidos de forma instantânea em processo sem reduzir a velocidade de corte do rebolo, e sem alterar o setup do processo de retificação. Visando-se otimizar o DICM, os principais fatores que apresentam influência sobre a resposta no domínio do tempo são analisados através de uma Análise Fatorial Fracionada. O DICM é validado correlacionando-se a informação quantitativa obtida da topografia, com as análises pós-processo de sinais de força de retificação e com medições da rugosidade efetiva do rebolo (parâmetro Rts). Abstract : A Dynamic In-process Characterization Method (DICM) based on acoustic emission (AE) is developed and validated, aiming at the in-process appraisal of the topography of conventional grinding wheels. For implementing the method, planned experiments are carried out by firstly developing an AE-based experimental rig with its particular software application. This enables to recognize shallow interferences amid the grinding wheel (vs= 30 m/s) and a diamond tip, in the elasticdeformation range of the tools. The on-line acquired AERAW signalsderived from such interferences are used as input data for signal processing techniques and a Neural Network (NN). Both analyses areimplemented out of the grinding process and therefore consist in aDynamic Post-process Characterization Method (DPCM). The DPCM´sresults are validated by measuring both the ground workpieces (i.e. roughness, microscopy, thermally affected layer and form deviation) and the replicas extracted from the grinding wheel´s topography. Based on the validated signal processing techniques proposed by the DPCM, the DICM is implemented. This is achieved by employing a transducer-fused experimental rig in which both AE and force signals are measured. The experimental rig and its developed software application enable in-process characterization of the topography of the conventional grinding wheel by extracting quantitative information from the AERAW signalswhich are on-line acquired during the interferences between the grinding wheel (vs= 30 m/s) and a diamond tip in a range of 1 µm. The quantified information associated with the grinding wheel´s topography is based on both a time domain and a frequency domain in-process analysis. Theresulting outputs from these analyses are obtained instantaneously in-process by neither interrupting the grinding process nor decelerating the grinding wheel´s cutting speed. In order to define an optimizedexperimental condition to assess the grinding wheel´s topography, the main factors which present direct influence on the time domain output were analyzed by using a Fractional Factorial Analysis. The DICM is validated by correlating the obtained quantified information from thegrinding wheel´s topography with both the post-process evaluation of the grinding cutting force and the post-process measurements of the effective roughness of the grinding wheel (parameter Rts)

Engineering Dynamics and Life Sciences

From Preface: This is the fourteenth time when the conference “Dynamical Systems: Theory and Applications” gathers a numerous group of outstanding scientists and engineers, who deal with widely understood problems of theoretical and applied dynamics. Organization of the conference would not have been possible without a great effort of the staff of the Department of Automation, Biomechanics and Mechatronics. The patronage over the conference has been taken by the Committee of Mechanics of the Polish Academy of Sciences and Ministry of Science and Higher Education of Poland. It is a great pleasure that our invitation has been accepted by recording in the history of our conference number of people, including good colleagues and friends as well as a large group of researchers and scientists, who decided to participate in the conference for the first time. With proud and satisfaction we welcomed over 180 persons from 31 countries all over the world. They decided to share the results of their research and many years experiences in a discipline of dynamical systems by submitting many very interesting papers. This year, the DSTA Conference Proceedings were split into three volumes entitled “Dynamical Systems” with respective subtitles: Vibration, Control and Stability of Dynamical Systems; Mathematical and Numerical Aspects of Dynamical System Analysis and Engineering Dynamics and Life Sciences. Additionally, there will be also published two volumes of Springer Proceedings in Mathematics and Statistics entitled “Dynamical Systems in Theoretical Perspective” and “Dynamical Systems in Applications”

A feature-based reverse engineering system using artificial neural networks

Reverse Engineering (RE) is the process of reconstructing CAD models from scanned data of a physical part acquired using 3D scanners. RE has attracted a great deal of research interest over the last decade. However, a review of the literature reveals that most research work have focused on creation of free form surfaces from point cloud data. Representing geometry in terms of surface patches is adequate to represent positional information, but can not capture any of the higher level structure of the part. Reconstructing solid models is of importance since the resulting solid models can be directly imported into commercial solid modellers for various manufacturing activities such as process planning, integral property computation, assembly analysis, and other applications. This research discusses the novel methodology of extracting geometric features directly from a data set of 3D scanned points, which utilises the concepts of artificial neural networks (ANNs). In order to design and develop a generic feature-based RE system for prismatic parts, the following five main tasks were investigated. (1) point data processing algorithms; (2) edge detection strategies; (3) a feature recogniser using ANNs; (4) a feature extraction module; (5) a CAD model exchanger into other CAD/CAM systems via IGES. A key feature of this research is the incorporation of ANN in feature recognition. The use of ANN approach has enabled the development of a flexible feature-based RE methodology that can be trained to deal with new features. ANNs require parallel input patterns. In this research, four geometric attributes extracted from a point set are input to the ANN module for feature recognition: chain codes, convex/concave, circular/rectangular and open/closed attribute. Recognising each feature requires the determination of these attributes. New and robust algorithms are developed for determining these attributes for each of the features. This feature-based approach currently focuses on solving the feature recognition problem based on 2.5D shapes such as block pocket, step, slot, hole, and boss, which are common and crucial in mechanical engineering products. This approach is validated using a set of industrial components. The test results show that the strategy for recognising features is reliable