Prognostics Design for Structural Health Management

J. Chiachio-Ruano, M. Chiachio Ruano, S. Sankararaman, A. Saxena, K. Goebel

Research output: Chapter in Book/Report/Conference proceedingChapter (peer-reviewed)

5 Citations (Scopus)

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.
LanguageEnglish
Title of host publicationEmerging Design Solutions in Structural Health Monitoring Systems
Editors Diego Alexander Tibaduiza Burgos, Luis Eduardo Mujica, Jose Rodelas
Chapter11
Pages234-273
Number of pages40
DOIs
Publication statusPublished - 2015

Fingerprint

Health
Graphite epoxy composites
Extrapolate
Physics
Graphite
Prediction
Epoxy
Fatigue of materials
Degradation
Fatigue
Efficacy
Maintenance
Schedule
Quantify
Composite
Denote
Uncertainty
Calculate
Modeling
Demonstrate

Keywords

  • prognostic techniques
  • structural health
  • fatigue data
  • graphite-epoxy composite
  • failure threshold
  • physics-based modeling

Cite this

Chiachio-Ruano, J., Chiachio Ruano, M., Sankararaman, S., Saxena, A., & Goebel, K. (2015). Prognostics Design for Structural Health Management. In D. A. Tibaduiza Burgos, L. E. Mujica, & J. Rodelas (Eds.), Emerging Design Solutions in Structural Health Monitoring Systems (pp. 234-273) https://doi.org/10.4018/978-1-4666-8490-4.ch011
Chiachio-Ruano, J. ; Chiachio Ruano, M. ; Sankararaman, S. ; Saxena, A. ; Goebel, K. / Prognostics Design for Structural Health Management. Emerging Design Solutions in Structural Health Monitoring Systems. editor / Diego Alexander Tibaduiza Burgos ; Luis Eduardo Mujica ; Jose Rodelas. 2015. pp. 234-273
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Chiachio-Ruano, J, Chiachio Ruano, M, Sankararaman, S, Saxena, A & Goebel, K 2015, Prognostics Design for Structural Health Management. in DA Tibaduiza Burgos, LE Mujica & J Rodelas (eds), Emerging Design Solutions in Structural Health Monitoring Systems. pp. 234-273. https://doi.org/10.4018/978-1-4666-8490-4.ch011

Prognostics Design for Structural Health Management. / Chiachio-Ruano, J.; Chiachio Ruano, M.; Sankararaman, S.; Saxena, A.; Goebel, K.

Emerging Design Solutions in Structural Health Monitoring Systems. ed. / Diego Alexander Tibaduiza Burgos; Luis Eduardo Mujica; Jose Rodelas. 2015. p. 234-273.

Research output: Chapter in Book/Report/Conference proceedingChapter (peer-reviewed)

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AB - 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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Chiachio-Ruano J, Chiachio Ruano M, Sankararaman S, Saxena A, Goebel K. Prognostics Design for Structural Health Management. In Tibaduiza Burgos DA, Mujica LE, Rodelas J, editors, Emerging Design Solutions in Structural Health Monitoring Systems. 2015. p. 234-273 https://doi.org/10.4018/978-1-4666-8490-4.ch011