Structural Health Monitoring

Author: Charles R. Farrar
Publisher: John Wiley & Sons
ISBN: 1118443217
Size: 18.23 MB
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Written by global leaders and pioneers in the field, this book is a must-have read for researchers, practicing engineers and university faculty working in SHM. Structural Health Monitoring: A Machine Learning Perspective is the first comprehensive book on the general problem of structural health monitoring. The authors, renowned experts in the field, consider structural health monitoring in a new manner by casting the problem in the context of a machine learning/statistical pattern recognition paradigm, first explaining the paradigm in general terms then explaining the process in detail with further insight provided via numerical and experimental studies of laboratory test specimens and in-situ structures. This paradigm provides a comprehensive framework for developing SHM solutions. Structural Health Monitoring: A Machine Learning Perspective makes extensive use of the authors’ detailed surveys of the technical literature, the experience they have gained from teaching numerous courses on this subject, and the results of performing numerous analytical and experimental structural health monitoring studies. Considers structural health monitoring in a new manner by casting the problem in the context of a machine learning/statistical pattern recognition paradigm Emphasises an integrated approach to the development of structural health monitoring solutions by coupling the measurement hardware portion of the problem directly with the data interrogation algorithms Benefits from extensive use of the authors’ detailed surveys of 800 papers in the technical literature and the experience they have gained from teaching numerous short courses on this subject.

Structural Health Monitoring

Author: Charles R. Farrar
Publisher: John Wiley & Sons
ISBN: 1119994330
Size: 78.91 MB
Format: PDF, Kindle
View: 2271
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Written by global leaders and pioneers in the field, this book is a must-have read for researchers, practicing engineers and university faculty working in SHM. Structural Health Monitoring: A Machine Learning Perspective is the first comprehensive book on the general problem of structural health monitoring. The authors, renowned experts in the field, consider structural health monitoring in a new manner by casting the problem in the context of a machine learning/statistical pattern recognition paradigm, first explaining the paradigm in general terms then explaining the process in detail with further insight provided via numerical and experimental studies of laboratory test specimens and in-situ structures. This paradigm provides a comprehensive framework for developing SHM solutions. Structural Health Monitoring: A Machine Learning Perspective makes extensive use of the authors’ detailed surveys of the technical literature, the experience they have gained from teaching numerous courses on this subject, and the results of performing numerous analytical and experimental structural health monitoring studies. Considers structural health monitoring in a new manner by casting the problem in the context of a machine learning/statistical pattern recognition paradigm Emphasises an integrated approach to the development of structural health monitoring solutions by coupling the measurement hardware portion of the problem directly with the data interrogation algorithms Benefits from extensive use of the authors’ detailed surveys of 800 papers in the technical literature and the experience they have gained from teaching numerous short courses on this subject.

Structural Health Monitoring

Author: Charles R. Farrar
Publisher: John Wiley & Sons
ISBN: 1118443209
Size: 50.56 MB
Format: PDF, ePub, Mobi
View: 5676
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Written by global leaders and pioneers in the field, this book is a must-have read for researchers, practicing engineers and university faculty working in SHM. Structural Health Monitoring: A Machine Learning Perspective is the first comprehensive book on the general problem of structural health monitoring. The authors, renowned experts in the field, consider structural health monitoring in a new manner by casting the problem in the context of a machine learning/statistical pattern recognition paradigm, first explaining the paradigm in general terms then explaining the process in detail with further insight provided via numerical and experimental studies of laboratory test specimens and in-situ structures. This paradigm provides a comprehensive framework for developing SHM solutions. Structural Health Monitoring: A Machine Learning Perspective makes extensive use of the authors’ detailed surveys of the technical literature, the experience they have gained from teaching numerous courses on this subject, and the results of performing numerous analytical and experimental structural health monitoring studies. Considers structural health monitoring in a new manner by casting the problem in the context of a machine learning/statistical pattern recognition paradigm Emphasises an integrated approach to the development of structural health monitoring solutions by coupling the measurement hardware portion of the problem directly with the data interrogation algorithms Benefits from extensive use of the authors’ detailed surveys of 800 papers in the technical literature and the experience they have gained from teaching numerous short courses on this subject.

Structural Health Monitoring

Author: Daniel Balageas
Publisher: John Wiley & Sons
ISBN: 0470394404
Size: 80.97 MB
Format: PDF, Mobi
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This book is organized around the various sensing techniques used to achieve structural health monitoring. Its main focus is on sensors, signal and data reduction methods and inverse techniques, which enable the identification of the physical parameters, affected by the presence of the damage, on which a diagnostic is established. Structural Health Monitoring is not oriented by the type of applications or linked to special classes of problems, but rather presents broader families of techniques: vibration and modal analysis; optical fibre sensing; acousto-ultrasonics, using piezoelectric transducers; and electric and electromagnetic techniques. Each chapter has been written by specialists in the subject area who possess a broad range of practical experience. The book will be accessible to students and those new to the field, but the exhaustive overview of present research and development, as well as the numerous references provided, also make it required reading for experienced researchers and engineers.

Structural Health Monitoring

Author: Ruqiang Yan
Publisher: Springer
ISBN: 331956126X
Size: 61.89 MB
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This book highlights the latest advances and trends in advanced signal processing (such as wavelet theory, time-frequency analysis, empirical mode decomposition, compressive sensing and sparse representation, and stochastic resonance) for structural health monitoring (SHM). Its primary focus is on the utilization of advanced signal processing techniques to help monitor the health status of critical structures and machines encountered in our daily lives: wind turbines, gas turbines, machine tools, etc. As such, it offers a key reference guide for researchers, graduate students, and industry professionals who work in the field of SHM.

Intelligent Sensor Networks

Author: Fei Hu
Publisher: CRC Press
ISBN: 1439892822
Size: 72.79 MB
Format: PDF
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Although governments worldwide have invested significantly in intelligent sensor network research and applications, few books cover intelligent sensor networks from a machine learning and signal processing perspective. Filling this void, Intelligent Sensor Networks: The Integration of Sensor Networks, Signal Processing and Machine Learning focuses on the close integration of sensing, networking, and smart signal processing via machine learning. Based on the world-class research of award-winning authors, the book provides a firm grounding in the fundamentals of intelligent sensor networks, including compressive sensing and sampling, distributed signal processing, and intelligent signal learning. Presenting recent research results of world-renowned sensing experts, the book is organized into three parts: Machine Learning—describes the application of machine learning and other AI principles in sensor network intelligence—covering smart sensor/transducer architecture and data representation for intelligent sensors Signal Processing—considers the optimization of sensor network performance based on digital signal processing techniques—including cross-layer integration of routing and application-specific signal processing as well as on-board image processing in wireless multimedia sensor networks for intelligent transportation systems Networking—focuses on network protocol design in order to achieve an intelligent sensor networking—covering energy-efficient opportunistic routing protocols for sensor networking and multi-agent-driven wireless sensor cooperation Maintaining a focus on "intelligent" designs, the book details signal processing principles in sensor networks. It elaborates on critical platforms for intelligent sensor networks and illustrates key applications—including target tracking, object identification, and structural health monitoring. It also includes a paradigm for validating the extent of spatiotemporal associations among data sources to enhance data cleaning in sensor networks, a sensor stream reduction application, and also considers the use of Kalman filters for attack detection in a water system sensor network that consists of water level sensors and velocity sensors.

Structural Health Monitoring 2015

Author: Fu-Kuo Chang
Publisher: DEStech Publications, Inc
ISBN: 1605952753
Size: 17.58 MB
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Proceedings of the Tenth International Workshop on Structural Health Monitoring, September 1–3, 2015. Selected research on the entire spectrum of structural health techniques and areas of applicationAvailable in print, complete online text download or individual articles. Series book comprising two volumes provides selected international research on the entire spectrum of structural health monitoring techniques used to diagnose and safeguard aircraft, vehicles, buildings, civil infrastructure, ships and railroads, as well as their components such as joints, bondlines, coatings and more. Includes special sections on system design, signal processing, multifunctional materials, sensor distribution, embedded sensors for monitoring composites, reliability and applicability in extreme environments. The extensive contents can be viewed below.

Emerging Design Solutions In Structural Health Monitoring Systems

Author: Diego Alexander Tibaduiza Burgos
Publisher: IGI Global
ISBN: 1466684917
Size: 40.47 MB
Format: PDF, Mobi
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Structural health monitoring is an exciting new field on the frontier of applied engineering. With the purpose of examining the health of standing buildings, aircrafts, and other complex structures using pre-installed sensors that provide stress and pressure data in real-time, this field allows structural-health experts to act as engineering âeoedoctors.âe These specialists diagnose potential problems in advance using complex algorithms and statistical modeling so that steps may be taken to prevent structural damageâe"or worse, loss of life. Emerging Design Solutions in Structural Health Monitoring Systems seeks to advance cutting-edge research in the field, with a special focus on cross-disciplinary work involving recent advances in IT. This research has enabled structural-health experts to wield groundbreaking new models of artificial intelligence as a diagnostic tool capable of identifying future problems before they even appear. This publication serves as a broad overview of structural management science, as well as an on-ramp for a general engineering audience, including students, educators, and researchers.

New Developments In Sensing Technology For Structural Health Monitoring

Author: Subhas Chandra Mukhopadhyay
Publisher: Springer Science & Business Media
ISBN: 3642210996
Size: 13.36 MB
Format: PDF, ePub
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The book has focussed on the different aspects of sensing technology, i.e. high reliability, adaptability, recalibration, information processing, data fusion, validation and integration of novel and high performance sensors specifically aims to use to inspect mechanical health of structure and similar applications. This book is dedicated to Sensing systems for Structural Health Monitoring offers to variety of users, namely, Master and PhD degree students, researchers, practitioners, especially Civil and Construction engineers. The book will provide an opportunity of a dedicated and a deep approach in order to improve their knowledge in this specific field.

Damage Prognosis

Author: Daniel J. Inman
Publisher: John Wiley & Sons
ISBN: 0470869089
Size: 39.12 MB
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Damage prognosis is a natural extension of damage detection and structural health monitoring and is forming a growing part of many businesses. This comprehensive volume presents a series of fundamental topics that define the new area of damage prognosis. Bringing together essential information in each of the basic technologies necessary to perform damage prognosis, it also reflects the highly interdisciplinary nature of the industry through the extensive referencing of each of the component disciplines. Taken from lectures given at the Pan American Advanced Studies Institute in Damage Prognosis sponsored by the US National Science Foundation in cooperation with Los Alamos National Laboratories, this book will be essential reading for anyone looking to get to grips with the fundamentals of damage prognosis. Presents the 'ground rules' for Damage Prognosis. Deals with interdisciplinary topics: rotating machines, aerospace structures, automotive components and civil structures. Covers essential technical material: equations, graphs and plots, tables and photographs. Offers additional material from the associated workshop on an active web site.