Prognostics and Health Management of Engineering Systems : an Introduction.

Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Kim, Nam-Ho
Otros Autores: An, Dawn, Choi, Joo-Ho
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Cham : Springer International Publishing, 2016.
Temas:
Engineering systems > Testing.
Engineering systems > Testing
Acceso en línea:Texto completo
Tabla de Contenidos:
  • 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.
  • 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.
  • 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.
  • 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).
  • 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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