Structural Health Monitoring

Author: Charles R. Farrar
Publisher: John Wiley & Sons
ISBN: 1118443217
Format: PDF, ePub, Docs
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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
Format: PDF, Docs
Download Now
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
Format: PDF, ePub, Docs
Download Now
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: Ruqiang Yan
Publisher: Springer
ISBN: 331956126X
Format: PDF, ePub
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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
Format: PDF, ePub, Mobi
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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.

Computational Techniques for Structural Health Monitoring

Author: Srinivasan Gopalakrishnan
Publisher: Springer Science & Business Media
ISBN: 9780857292841
Format: PDF, ePub, Mobi
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The increased level of activity on structural health monitoring (SHM) in various universities and research labs has resulted in the development of new methodologies for both identifying the existing damage in structures and predicting the onset of damage that may occur during service. Designers often have to consult a variety of textbooks, journal papers and reports, because many of these methodologies require advanced knowledge of mechanics, dynamics, wave propagation, and material science. Computational Techniques for Structural Health Monitoring gives a one-volume, in-depth introduction to the different computational methodologies available for rapid detection of flaws in structures. Techniques, algorithms and results are presented in a way that allows their direct application. A number of case studies are included to highlight further the practical aspects of the selected topics. Computational Techniques for Structural Health Monitoring also provides the reader with numerical simulation tools that are essential to the development of novel algorithms for the interpretation of experimental measurements, and for the identification of damage and its characterization. Upon reading Computational Techniques for Structural Health Monitoring, graduate students will be able to begin research-level work in the area of structural health monitoring. The level of detail in the description of formulation and implementation also allows engineers to apply the concepts directly in their research.

Human and Machine Learning

Author: Jianlong Zhou
Publisher: Springer
ISBN: 3319904035
Format: PDF
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With an evolutionary advancement of Machine Learning (ML) algorithms, a rapid increase of data volumes and a significant improvement of computation powers, machine learning becomes hot in different applications. However, because of the nature of “black-box” in ML methods, ML still needs to be interpreted to link human and machine learning for transparency and user acceptance of delivered solutions. This edited book addresses such links from the perspectives of visualisation, explanation, trustworthiness and transparency. The book establishes the link between human and machine learning by exploring transparency in machine learning, visual explanation of ML processes, algorithmic explanation of ML models, human cognitive responses in ML-based decision making, human evaluation of machine learning and domain knowledge in transparent ML applications. This is the first book of its kind to systematically understand the current active research activities and outcomes related to human and machine learning. The book will not only inspire researchers to passionately develop new algorithms incorporating human for human-centred ML algorithms, resulting in the overall advancement of ML, but also help ML practitioners proactively use ML outputs for informative and trustworthy decision making. This book is intended for researchers and practitioners involved with machine learning and its applications. The book will especially benefit researchers in areas like artificial intelligence, decision support systems and human-computer interaction.

Structural Health Monitoring

Author: Ruqiang Yan
Publisher: Springer
ISBN: 331956126X
Format: PDF, ePub, Mobi
Download Now
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.

Structural Health Monitoring Damage Detection Volume 7

Author: Christopher Niezrecki
Publisher: Springer
ISBN: 3319541099
Format: PDF, ePub, Mobi
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Structural Health Monitoring & Damage Detection, Volume 7: Proceedings of the 35th IMAC, A Conference and Exposition on Structural Dynamics, 2017, the seventh volume of ten from the Conference brings together contributions to this important area of research and engineering. The collection presents early findings and case studies on fundamental and applied aspects of Structural Health Monitoring & Damage Detection, including papers on: Structural Health Monitoring Damage Detection System Identification Active Controls

Emerging Design Solutions in Structural Health Monitoring Systems

Author: Diego Alexander Tibaduiza Burgos
Publisher: IGI Global
ISBN: 1466684917
Format: PDF, ePub, Docs
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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.