Practical options for selecting data-driven or physics-based prognostics algorithms with reviews

This paper is to provide practical options for prognostics so that beginners can select appropriate methods for their fields of application. To achieve this goal, several popular algorithms are first reviewed in the data-driven and physics-based prognostics methods. Each algorithm’s attributes and p...

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Veröffentlicht in:	Reliability engineering & system safety Jg. 133; S. 223 - 236
Hauptverfasser:	An, Dawn, Kim, Nam H., Choi, Joo-Ho
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Oxford Elsevier Ltd 01.01.2015 Elsevier
Schlagworte:	Algorithms Applied sciences Bayesian inference Bias Complexity Data-driven prognostics Exact sciences and technology Fatigue failure Fracture mechanics (crack, fatigue, damage...) Fundamental areas of phenomenology (including applications) Gaussian process regression Mathematics Neural network Neural networks Noise Operational research and scientific management Operational research. Management science Parametric inference Particle filter Physics Physics-based prognostics Probability and statistics Reliability engineering Reliability theory. Replacement problems Sampling theory, sample surveys Sciences and techniques of general use Solid mechanics Statistics Structural and continuum mechanics Physics-based prognostics Gaussian process regression Neural network Bayesian inference Data-driven prognostics Particle filter Availability Bayes estimation Monte Carlo method Parameter estimation Contour line Regression analysis Review Data driven modelling Modeling Fatigue crack Noise level Crack propagation Particle method Gaussian process Model matching Covariance Physical model Damaging
ISSN:	0951-8320, 1879-0836
Online-Zugang:	Volltext
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