TY - JOUR
T1 - Resiliency analysis for complex engineered system design
AU - Mehrpouyan, Hoda
AU - Haley, Brandon
AU - Dong, Andy
AU - Tumer, Irem Y.
AU - Hoyle, Christopher
AU - Turner, Irem Y.
N1 - Publisher Copyright:
Copyright © 2015 Cambridge University Press.
PY - 2014/4/1
Y1 - 2014/4/1
N2 - Resilience is a key driver in the design of systems that must operate in an uncertain operating environment, and it 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. Nonlinear dynamical system 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, which is an electrical power system developed at NASA Ames as a subsystem for the ramp system of an infantry fighting vehicle.
AB - Resilience is a key driver in the design of systems that must operate in an uncertain operating environment, and it 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. Nonlinear dynamical system 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, which is an electrical power system developed at NASA Ames as a subsystem for the ramp system of an infantry fighting vehicle.
KW - Complex System Design
KW - Failure Density
KW - Failure Propagation
KW - Robust Design
UR - http://www.scopus.com/inward/record.url?scp=84921319311&partnerID=8YFLogxK
UR - http://dx.doi.org/10.1017/S0890060414000663
U2 - 10.1017/S0890060414000663
DO - 10.1017/S0890060414000663
M3 - Article
AN - SCOPUS:84921319311
SN - 0890-0604
VL - 29
SP - 93
EP - 108
JO - Artificial Intelligence for Engineering Design, Analysis and Manufacturing
JF - Artificial Intelligence for Engineering Design, Analysis and Manufacturing
IS - 1
ER -