LEADER 05194nam 2200613 a 450 001 9911019192403321 005 20200520144314.0 010 $a9781118443118 010 $a111844311X 010 $a9781299186781 010 $a1299186785 010 $a9781118443200 010 $a1118443209 010 $a9781118443217 010 $a1118443217 035 $a(CKB)2670000000327450 035 $a(EBL)1118500 035 $a(OCoLC)827208386 035 $a(MiAaPQ)EBC1118500 035 $a(DLC) 2012025939 035 $a(Perlego)1013487 035 $a(EXLCZ)992670000000327450 100 $a20120622d2013 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aStructural health monitoring $ea machine learning perspective /$fCharles R. Farrar, Keith Worden 210 $aChichester, West Sussex, U.K. ;$aHoboken, N.J. $cWiley$d2013 215 $a1 online resource (655 p.) 300 $aDescription based upon print version of record. 311 08$a9781119994336 311 08$a1119994330 320 $aIncludes bibliographical references and index. 327 $aSTRUCTURAL 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 327 $a1.7 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 327 $a2.2.4 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 327 $a2.4.2 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 327 $a3.7 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 327 $a4.6.1 Sensor Arrays Directly Connected to Central Processing Hardware 330 $aWritten 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 606 $aStructural health monitoring 615 0$aStructural health monitoring. 676 $a624.1/71 700 $aFarrar$b C. R$g(Charles R.)$0520748 701 $aWorden$b K$0520749 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9911019192403321 996 $aStructural health monitoring$94420519 997 $aUNINA