Record Details
Resiliency analysis for complex engineered system design
ScholarsArchive at Oregon State University
| Field | Value |
|---|---|
| Title | Resiliency analysis for complex engineered system design |
| Names |
Mehrpouyan, Hoda
(creator) Haley, Brandon (creator) Dong, Andy (creator) Tumer, Irem Y. (creator) Hoyle, Christopher (creator) |
| Date Issued | 2015-02 (iso8601) |
| Note | This is an author's peer-reviewed final manuscript, as accepted by the publisher. The published article is copyrighted by Cambridge University Press and can be found at: http://journals.cambridge.org/action/displayJournal?jid=AIE |
| Abstract | Resilience is a key driver in the design of systems that must operate in an uncertain operating environment, and is a key metric to assess the capacity for systems to perform within the specified performance envelop despite disturbances to their operating environment. This paper describes a graph spectral approach to calculate the resilience of complex engineered systems. The resilience of the design architecture of complex engineered systems is deduced from graph spectra. This is calculated from adjacency matrix representations of the physical connections between components in complex engineered systems. Furthermore, we propose a new method to identify the most vulnerable components in the design and design architectures that are robust to transmission of failures. Non-linear dynamical system (NLDS) and epidemic spreading models are used to compare the failure propagation mean time transformation. Using these metrics, we present a case study based on the Advanced Diagnostics and Prognostics Testbed (ADAPT), which is an Electrical Power System (EPS) developed at NASA Ames as a subsystem for the Ramp System of an Infantry Fighting Vehicle (IFV). |
| Genre | Article |
| Topic | Robust Design |
| Identifier | Mehrpouyan, H., Haley, B., Dong, A., Tumer, I. Y., & Hoyle, C. (2015). Resiliency analysis for complex engineered system design. Artificial Intelligence for Engineering Design, Analysis and Manufacturing, 29(01), 93-108. doi.10.1017/S0890060414000663 |
