000 06488cam a2200625 i 4500
001 on1043051822
003 OCoLC
005 20220711203537.0
006 m o d
007 cr |||||||||||
008 180702s2018 nju ob 001 0 eng
010 _a 2018031572
040 _aDLC
_beng
_erda
_epn
_cDLC
_dOCLCF
_dN$T
_dEBLCP
_dYDX
_dCNCGM
_dSTF
_dDKU
_dGZM
_dDLC
_dOCLCO
_dU3W
_dOCLCQ
_dSFB
_dOCLCQ
019 _a1048895928
_a1049605644
020 _a9781119515357
_q(ePub)
020 _a1119515351
020 _a9781119515302
_q(Adobe PDF)
020 _a1119515300
020 _a9781119515326
_q(electronic bk.)
020 _a1119515327
_q(electronic bk.)
020 _z9781119515333
_q(hardcover)
020 _z1119515335
035 _a(OCoLC)1043051822
_z(OCoLC)1048895928
_z(OCoLC)1049605644
042 _apcc
050 1 0 _aTK7870
072 7 _aTEC
_x009070
_2bisacsh
082 0 0 _a621.381028/8
_223
049 _aMAIN
245 0 0 _aPrognostics and health management of electronics :
_bfundamentals, machine learning, and internet of things /
_cedited by Michael Pecht, Ph. D., PE, Myeongsu Kang, Ph. D.
250 _aSecond edition.
264 1 _aHoboken, NJ :
_bJohn Wiley & Sons,
_c2018.
300 _a1 online resource
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
504 _aIncludes bibliographical references and index.
588 0 _aPrint version record and CIP data provided by publisher.
505 0 _aCover; Title Page; Copyright; About the Editors; Contents; List of Contributors; Preface; About the Contributors; Acknowledgment; List of Abbreviations; Chapter 1 Introduction to PHM; 1.1 Reliability and Prognostics; 1.2 PHM for Electronics; 1.3 PHM Approaches; 1.3.1 PoF-Based Approach; 1.3.1.1 Failure Modes, Mechanisms, and Effects Analysis (FMMEA); 1.3.1.2 Life-Cycle Load Monitoring; 1.3.1.3 Data Reduction and Load Feature Extraction; 1.3.1.4 Data Assessment and Remaining Life Calculation; 1.3.1.5 Uncertainty Implementation and Assessment; 1.3.2 Canaries; 1.3.3 Data-Driven Approach.
505 8 _a1.3.3.1 Monitoring and Reasoning of Failure Precursors1.3.3.2 Data Analytics and Machine Learning; 1.3.4 Fusion Approach; 1.4 Implementation of PHM in a System of Systems; 1.5 PHM in the Internet of Things (IoT) Era; 1.5.1 IoT-Enabled PHM Applications: Manufacturing; 1.5.2 IoT-Enabled PHM Applications: Energy Generation; 1.5.3 IoT-Enabled PHM Applications: Transportation and Logistics; 1.5.4 IoT-Enabled PHM Applications: Automobiles; 1.5.5 IoT-Enabled PHM Applications: Medical Consumer Products; 1.5.6 IoT-Enabled PHM Applications: Warranty Services.
505 8 _a1.5.7 IoT-Enabled PHM Applications: Robotics1.6 Summary; References; Chapter 2 Sensor Systems for PHM; 2.1 Sensor and Sensing Principles; 2.1.1 Thermal Sensors; 2.1.2 Electrical Sensors; 2.1.3 Mechanical Sensors; 2.1.4 Chemical Sensors; 2.1.5 Humidity Sensors; 2.1.6 Biosensors; 2.1.7 Optical Sensors; 2.1.8 Magnetic Sensors; 2.2 Sensor Systems for PHM; 2.2.1 Parameters to be Monitored; 2.2.2 Sensor System Performance; 2.2.3 Physical Attributes of Sensor Systems; 2.2.4 Functional Attributes of Sensor Systems; 2.2.4.1 Onboard Power and Power Management.
505 8 _a2.2.4.2 Onboard Memory and Memory Management2.2.4.3 Programmable Sampling Mode and Sampling Rate; 2.2.4.4 Signal Processing Software; 2.2.4.5 Fast and Convenient Data Transmission; 2.2.5 Reliability; 2.2.6 Availability; 2.2.7 Cost; 2.3 Sensor Selection; 2.4 Examples of Sensor Systems for PHM Implementation; 2.5 Emerging Trends in Sensor Technology for PHM; References; Chapter 3 Physics-of-Failure Approach to PHM; 3.1 PoF-Based PHM Methodology; 3.2 Hardware Configuration; 3.3 Loads; 3.4 Failure Modes, Mechanisms, and Effects Analysis (FMMEA); 3.4.1 Examples of FMMEA for Electronic Devices.
505 8 _a3.5 Stress Analysis3.6 Reliability Assessment and Remaining-Life Predictions; 3.7 Outputs from PoF-Based PHM; 3.8 Caution and Concerns in the Use of PoF-Based PHM; 3.9 Combining PoF with Data-Driven Prognosis; References; Chapter 4 Machine Learning: Fundamentals; 4.1 Types of Machine Learning; 4.1.1 Supervised, Unsupervised, Semi-Supervised, and Reinforcement Learning; 4.1.2 Batch and Online Learning; 4.1.3 Instance-Based and Model-Based Learning; 4.2 Probability Theory in Machine Learning: Fundamentals; 4.2.1 Probability Space and Random Variables.
520 _aAN INDISPENSABLE GUIDE FOR ENGINEERS AND DATA SCIENTISTS IN DESIGN, TESTING, OPERATION, MANUFACTURING, AND MAINTENANCE A road map to the current challenges and available opportunities for the research and development of Prognostics and Health Management "PHM", this important work covers all areas of electronics and explains how to: . assess methods for damage estimation of components and systems due to field loading conditions. assess the cost and benefits of prognostic implementations. develop novel methods for in situ monitoring of products and systems in actual life-cycle conditions. enable condition-based "predictive" maintenance. increase system availability through an extension of maintenance cycles and/or timely repair actions. obtain knowledge of load history for future design, qualification, and root cause analysis. reduce the occurrence of no fault found "NFF". subtract life-cycle costs of equipment from reduction in inspection costs, downtime, and inventory Prognostics and Health Management of Electronics also explains how to understand statistical techniques and machine learning methods used for diagnostics and prognostics. Using this valuable resource, electrical engineers, data scientists, and design engineers will be able to fully grasp the synergy between IoT, machine learning, and risk assessment.
650 0 _aElectronic systems
_xMaintenance and repair.
_98642
650 7 _aTECHNOLOGY & ENGINEERING
_xMechanical.
_2bisacsh
_98643
650 7 _aElectronic systems
_xMaintenance and repair.
_2fast
_0(OCoLC)fst00907488
_98642
655 0 _aElectronic books.
_93294
655 4 _aElectronic books.
_93294
700 1 _aPecht, Michael,
_eeditor.
_98644
700 1 _aKang, Myeongsu,
_d1980-
_eeditor.
_98645
776 0 8 _iPrint version:
_tPrognostics and health management of electronics.
_bSecond edition.
_dHoboken, NJ : John Wiley & Sons, 2018
_z9781119515333
_w(DLC) 2018029737
856 4 0 _uhttps://doi.org/10.1002/9781119515326
_zWiley Online Library
942 _cEBK
994 _aC0
_bDG1
999 _c69159
_d69159