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161112s2016 xx o 000 0 eng d |
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|a EBLCP
|b eng
|e pn
|c EBLCP
|d OCLCQ
|d IDB
|d OCLCF
|d OCLCO
|d OCLCQ
|d UKAHL
|d OCLCQ
|d OCLCO
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|a 9783319447421
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|a 3319447424
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|a (OCoLC)961453590
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|a TA1-2040
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|a 620
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|a UAMI
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|a Kim, Nam-Ho.
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|a Prognostics and Health Management of Engineering Systems :
|b an Introduction.
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| 260 |
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|a Cham :
|b Springer International Publishing,
|c 2016.
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| 300 |
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|a 1 online resource (355 pages)
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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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|a online resource
|b cr
|2 rdacarrier
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|a Print version record.
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|a Preface; Contents; 1 Introduction; 1.1 Prognostics and Health Management; 1.2 Historical Background; 1.3 PHM Applications; 1.4 Review of Prognostics Algorithms; 1.5 Benefits and Challenges for Prognostics; 1.5.1 Benefits in Life-Cycle Cost; 1.5.2 Benefits in System Design and Development; 1.5.3 Benefits in Production; 1.5.4 Benefits in System Operation; 1.5.5 Benefits in Logistics Support and Maintenance; 1.5.6 Challenges in Prognostics; References; 2 Tutorials for Prognostics; 2.1 Introduction; 2.2 Prediction of Degradation Behavior; 2.2.1 Least Squares Method.
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|a 2.2.2 When a Degradation Model Is Available (Physics-Based Approaches)2.2.2.1 Problem Definition; 2.2.2.2 Parameter Estimation and Degradation Prediction; 2.2.2.3 Effect of Noise in Data; 2.2.3 When a Degradation Model Is NOT Available (Data-Driven Approaches); 2.2.3.1 Function Evaluation; 2.2.3.2 Overfitting; 2.2.3.3 Prognosis with More Training Data; 2.3 RUL Prediction; 2.3.1 RUL; 2.3.2 Prognostics Metrics; 2.3.2.1 Prognostic Horizon (PH); 2.3.2.2 \varvec{ \alpha { -- }\lambda} Accuracy; 2.3.2.3 (Cumulative) Relative Accuracy (RA, CRA); 2.3.2.4 Convergence; 2.3.2.5 Results with MATLAB Code.
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|a 2.4 Uncertainty2.5 Issues in Practical Prognostics; 2.6 Exercises; References; 3 Bayesian Statistics for Prognostics; 3.1 Introduction to Bayesian Theory; 3.2 Aleatory Uncertainty versus Epistemic Uncertainty; 3.2.1 Aleatory Uncertainty; 3.2.2 Epistemic Uncertainty; 3.2.3 Sampling Uncertainty in Coupon Tests; 3.3 Conditional Probability and Total Probability; 3.3.1 Conditional Probability; 3.3.2 Total Probability; 3.4 Bayes' Theorem; 3.4.1 Bayes' Theorem in Probability Form; 3.4.2 Bayes' Theorem in Probability Density Form; 3.4.3 Bayes' Theorem with Multiple Data.
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|a 3.4.4 Bayes' Theorem for Parameter Estimation3.5 Bayesian Updating; 3.5.1 Recursive Bayesian Update; 3.5.2 Overall Bayesian Update; 3.6 Bayesian Parameter Estimation; 3.7 Generating Samples from Posterior Distribution; 3.7.1 Inverse CDF Method; 3.7.2 Grid Approximation Method: One Parameter; 3.7.3 Grid Approximation: Two Parameters; 3.8 Exercises; References; 4 Physics-Based Prognostics; 4.1 Introduction to Physics-Based Prognostics; 4.1.1 Demonstration Problem: Battery Degradation; 4.2 Nonlinear Least Squares (NLS).
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|a 4.2.1 MATLAB Implementation of Battery Degradation Prognostics Using Nonlinear Least Squares4.2.1.1 Problem Definition (Lines 5-14, 48); 4.2.1.2 Prognosis Using NLS (Lines 17-32); 4.2.1.3 Post-processing (Lines 34-37); 4.3 Bayesian Method (BM); 4.3.1 Markov Chain Monte Carlo (MCMC) Sampling Method; 4.3.2 MATLAB Implementation of Bayesian Method for Battery Prognostics; 4.3.2.1 Problem Definition (Lines 5-16, 52); 4.3.2.2 Prognosis Using BM with MCMC (Lines 19-41); 4.3.2.3 Post-processing (Lines 43-46); 4.4 Particle Filter (PF); 4.4.1 SIR Process.
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| 500 |
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|a 4.4.2 MATLAB Implementation of Battery Prognostics.
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| 590 |
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|a ProQuest Ebook Central
|b Ebook Central Academic Complete
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| 650 |
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|a Engineering systems
|x Testing.
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| 650 |
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7 |
|a Engineering systems
|x Testing
|2 fast
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| 700 |
1 |
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|a An, Dawn.
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| 700 |
1 |
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|a Choi, Joo-Ho.
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| 776 |
0 |
8 |
|i Print version:
|a Kim, Nam-Ho.
|t Prognostics and Health Management of Engineering Systems : An Introduction.
|d Cham : Springer International Publishing, ©2016
|z 9783319447407
|
| 856 |
4 |
0 |
|u https://ebookcentral.uam.elogim.com/lib/uam-ebooks/detail.action?docID=4723678
|z Texto completo
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|a Askews and Holts Library Services
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|b EBLB
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