Prognostics Design for Structural Health Management

Prognostics Design for Structural Health Management

J. Chiachío (University of Granada, Spain), M. Chiachío (University of Granada, Spain), S. Sankararaman (SGT Inc., USA & NASA Ames Research Center, USA), A. Saxena (SGT Inc., USA & NASA Ames Research Center, USA) and K. Goebel (NASA Ames Research Center, USA)
Copyright: © 2015 |Pages: 40
DOI: 10.4018/978-1-4666-8490-4.ch011
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Abstract

The chapter describes the application of prognostic techniques to the domain of structural health and demonstrates the efficacy of the methods using fatigue data from a graphite-epoxy composite coupon. Prognostics denotes the in-situ assessment of the health of a component and the repeated estimation of remaining life, conditional on anticipated future usage. The methods shown here use a physics-based modeling approach whereby the behavior of the damaged components is encapsulated via mathematical equations that describe the characteristics of the components as it experiences increasing degrees of degradation. Mathematical rigorous techniques are used to extrapolate the remaining life to a failure threshold. Additionally, mathematical tools are used to calculate the uncertainty associated with making predictions. The information stemming from the predictions can be used in an operational context for go/no go decisions, quantify risk of ability to complete a (set of) mission or operation, and when to schedule maintenance.
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Fundamentals

Predictive information about a component fault/damage can be a valuable resource in determining an appropriate course of action to avoid failures. Potential of prognostics in positively contributing to safety and improving life-cycle costs is equally relevant to existing legacy systems and new system designs. Legacy systems adopt additional sensing and processing with a potentially high price of retrofitting and additional validation and/or certification costs to gain extended system life and safety factor. New system designs can significantly reduce these costs if prognostics and health management are adopted early in the design to facilitate a more optimal sensor placement for observability and coverage. This, however, requires integration of health management design into the systems engineering process. The following section briefly discusses various design considerations involved in design and development of PHM.

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