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Accession Number N20130011505
Title Analytical Algorithms to Quantify the Uncertainty in Remaining Useful Life Prediction.
Publication Date Mar 2013
Media Count 11p
Personal Author A. Saxena K. Goebel M. Daigle S. Sankararaman
Abstract This paper investigates the use of analytical algorithms to quantify the uncertainty in the remaining useful life (RUL) estimate of components used in aerospace applications. The prediction of RUL is affected by several sources of uncertainty and it is important to systematically quantify their combined effect by computing the uncertainty in the RUL prediction in order to aid risk assessment, risk mitigation, and decisionmaking. While sampling-based algorithms have been conventionally used for quantifying the uncertainty in RUL, analytical algorithms are computationally cheaper and sometimes, are better suited for online decision-making. While exact analytical algorithms are available only for certain special cases (for e.g., linear models with Gaussian variables), effective approximations can be made using the the first-order second moment method (FOSM), the first-order reliability method (FORM), and the inverse first-order reliability method (Inverse FORM). These methods can be used not only to calculate the entire probability distribution of RUL but also to obtain probability bounds on RUL. This paper explains these three methods in detail and illustrates them using the state-space model of a lithium-ion battery.
Keywords Aerospace engineering
Algorithms
Electric batteries
Life(Durability)
Lithium
Metal ions
Method of moments
Predictions
Probability theory
Reliability
Risk assessment
Sampling


 
Source Agency National Aeronautics and Space Administration
NTIS Subject Category 72F - Statistical Analysis
97M - Batteries & Components
Corporate Author National Aeronautics and Space Administration, Moffett Field, CA. Ames Research Center.
Document Type Conference proceedings
Title Note N/A
NTIS Issue Number 1321
Contract Number NNA08CG83C

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