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|a 1048895928
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|a 9781119515357
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|a 1119515351
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|a 10.1002/9781119515326
|2 doi
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|a (OCoLC)1043051822
|z (OCoLC)1048895928
|z (OCoLC)1049605644
|z (OCoLC)1167525925
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|a 8470990
|b IEEE
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|a TEC
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|a HCDD
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|a Prognostics and health management of electronics :
|b fundamentals, machine learning, and internet of things /
|c edited by Michael Pecht, Ph. D., PE, Myeongsu Kang, Ph. D.
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| 250 |
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|a Second edition.
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| 264 |
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1 |
|a Hoboken, NJ :
|b John Wiley & Sons,
|c 2018.
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| 300 |
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|a 1 online resource
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| 336 |
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|a text
|b txt
|2 rdacontent
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| 337 |
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|a computer
|b c
|2 rdamedia
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| 338 |
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|a online resource
|b cr
|2 rdacarrier
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| 504 |
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|a Includes bibliographical references and index.
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|a Print version record and CIP data provided by publisher.
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|a Cover; 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.
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|a 1.3.3.1 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.
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|a 1.5.7 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.
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|a 2.2.4.2 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.
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|a 3.5 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.
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| 520 |
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|a AN 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.
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| 650 |
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|a Electronic systems
|x Maintenance and repair.
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| 650 |
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7 |
|a TECHNOLOGY & ENGINEERING
|x Mechanical.
|2 bisacsh
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| 650 |
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7 |
|a Electronic systems
|x Maintenance and repair
|2 fast
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| 700 |
1 |
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|a Pecht, Michael,
|e editor.
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| 700 |
1 |
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|a Kang, Myeongsu,
|d 1980-
|e editor.
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| 758 |
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|i has work:
|a Prognostics and health management of electronics (Text)
|1 https://id.oclc.org/worldcat/entity/E39PCGMFqGdmq8qp9xkccbPtgC
|4 https://id.oclc.org/worldcat/ontology/hasWork
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| 776 |
0 |
8 |
|i Print version:
|t Prognostics and health management of electronics.
|b Second edition.
|d Hoboken, NJ : John Wiley & Sons, 2018
|z 9781119515333
|w (DLC) 2018029737
|
| 856 |
4 |
0 |
|u https://ebookcentral.proquest.com/lib/holycrosscollege-ebooks/detail.action?docID=5494680
|y Click for online access
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|a EBC-AC
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|a 92
|b HCD
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