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Record Nr. |
UNINA9910141509303321 |
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Autore |
Farrar C. R (Charles R.) |
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Titolo |
Structural health monitoring [[electronic resource] ] : a machine learning perspective / / Charles R. Farrar, Keith Worden |
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Pubbl/distr/stampa |
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Chichester, West Sussex, U.K. ; ; Hoboken, N.J., : Wiley, 2013 |
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ISBN |
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1-118-44311-X |
1-299-18678-5 |
1-118-44320-9 |
1-118-44321-7 |
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Descrizione fisica |
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1 online resource (655 p.) |
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Altri autori (Persone) |
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Disciplina |
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Soggetti |
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Structural health monitoring |
Electronic books. |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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Description based upon print version of record. |
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Nota di bibliografia |
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Includes bibliographical references and index. |
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Nota di contenuto |
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STRUCTURAL HEALTH MONITORING; Contents; Preface; Acknowledgements; 1 Introduction; 1.1 How Engineers and Scientists Study Damage; 1.2 Motivation for Developing SHM Technology; 1.3 Definition of Damage; 1.4 A Statistical Pattern Recognition Paradigm for SHM; 1.4.1 Operational Evaluation; 1.4.2 Data Acquisition; 1.4.3 Data Normalisation; 1.4.4 Data Cleansing; 1.4.5 Data Compression; 1.4.6 Data Fusion; 1.4.7 Feature Extraction; 1.4.8 Statistical Modelling for Feature Discrimination; 1.5 Local versus Global Damage Detection; 1.6 Fundamental Axioms of Structural Health Monitoring |
1.7 The Approach Taken in This BookReferences; 2 Historical Overview; 2.1 Rotating Machinery Applications; 2.1.1 Operational Evaluation for Rotating Machinery; 2.1.2 Data Acquisition for Rotating Machinery; 2.1.3 Feature Extraction for Rotating Machinery; 2.1.4 Statistical Modelling for Damage Detection in Rotating Machinery; 2.1.5 Concluding Comments about Condition Monitoring of Rotating Machinery; 2.2 Offshore Oil Platforms; 2.2.1 Operational Evaluation for Offshore Platforms; 2.2.2 Data Acquisition for Offshore Platforms; 2.2.3 Feature Extraction for Offshore Platforms |
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2.2.4 Statistical Modelling for Offshore Platforms2.2.5 Lessons Learned from Offshore Oil Platform Structural Health Monitoring Studies; 2.3 Aerospace Structures; 2.3.1 Operational Evaluation for Aerospace Structures; 2.3.2 Data Acquisition for Aerospace Structures; 2.3.3 Feature Extraction and Statistical Modelling for Aerospace Structures; 2.3.4 Statistical Models Used for Aerospace SHM Applications; 2.3.5 Concluding Comments about Aerospace SHM Applications; 2.4 Civil Engineering Infrastructure; 2.4.1 Operational Evaluation for Bridge Structures |
2.4.2 Data Acquisition for Bridge Structures2.4.3 Features Based on Modal Properties; 2.4.4 Statistical Classification of Features for Civil Engineering Infrastructure; 2.4.5 Applications to Bridge Structures; 2.5 Summary; References; 3 Operational Evaluation; 3.1 Economic and Life-Safety Justifications for Structural Health Monitoring; 3.2 Defining the Damage to Be Detected; 3.3 The Operational and Environmental Conditions; 3.4 Data Acquisition Limitations; 3.5 Operational Evaluation Example: Bridge Monitoring; 3.6 Operational Evaluation Example: Wind Turbines |
3.7 Concluding Comment on Operational EvaluationReferences; 4 Sensing and Data Acquisition; 4.1 Introduction; 4.2 Sensing and Data Acquisition Strategies for SHM; 4.2.1 Strategy I; 4.2.2 Strategy II; 4.3 Conceptual Challenges for Sensing and Data Acquisition Systems; 4.4 What Types of Data Should Be Acquired?; 4.4.1 Dynamic Input and Response Quantities; 4.4.2 Other Damage-Sensitive Physical Quantities; 4.4.3 Environmental Quantities; 4.4.4 Operational Quantities; 4.5 Current SHM Sensing Systems; 4.5.1 Wired Systems; 4.5.2 Wireless Systems; 4.6 Sensor Network Paradigms |
4.6.1 Sensor Arrays Directly Connected to Central Processing Hardware |
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Sommario/riassunto |
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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 |
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2. |
Record Nr. |
UNINA9910700805403321 |
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Autore |
Hansen Cristi V |
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Titolo |
Status of ground-water levels and storage volume in the Equus Beds aquifer near Wichita, Kansas, July 2010 [[electronic resource] /] / by Cristi V. Hansen ; prepared in cooperation with the city of Wichita, Kansas |
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Pubbl/distr/stampa |
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Reston, Va. : , : U.S. Dept. of the Interior, U.S. Geological Survey, , 2011 |
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Descrizione fisica |
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1 online resource (1 map.) : color |
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Collana |
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Scientific investigations map ; ; 3159 |
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Soggetti |
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Groundwater - Kansas - Wichita |
Water table - Kansas - Wichita |
Maps. |
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Lingua di pubblicazione |
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Formato |
Materiale cartografico a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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Depths shown by isolines and soundings. |
Title from title screen (viewed on Aug. 25, 2011). |
Includes text, location map, graph and table. |
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Nota di bibliografia |
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Includes bibliographical references. |
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