Resilient Design of Complex Engineered Systems Against Cascading Failure

2013 ◽  
Author(s):  
Hoda Mehrpouyan ◽  
Brandon Haley ◽  
Andy Dong ◽  
Irem Y. Tumer ◽  
Chris Hoyle
Keyword(s):  
Complex Networks ◽  
System Design ◽  
Electrical Power ◽  
Electrical Power System ◽  
Complex Engineered Systems ◽  
Failure Propagation ◽  
Resilient Design ◽  
Engineered Systems ◽  
Design Characteristics ◽  
Energy Signal

This paper describes an approach commonly used with complex networks to study the failure propagation in an engineered system design. The goal of the research is to synthesize and illustrate system design characteristics that results from possible impact of the underlying design methodology based on cascading failures. Further, identifying the most vulnerable component in the design or system design architectures that are resilient to such dissemination of failures provide additional property improvement for resilient design. The paper presents a case study based on the ADAPT (Electrical Power System) EPS testbed at NASA Ames as a subsystem for the Ramp System of an Infantry Fighting Vehicle (IFV). A popular methodology based on the adjacency matrix, which is commonly used to represent edge connections between nodes in complex networks, has inspired interest in the use of similar methods to represent complex engineered systems. This is made possible, by defining the connections between components as a flow of energy, signal, and material and constraining physical connection between compatible components within complex engineered systems. Non-linear dynamical system (NLDS) and epidemic spreading models are used to compare the failure propagation mean time transformation. The results show that coupling, modularity, and module complexity all play an important part in the design of robust large complex engineered systems.

scholarly journals Resiliency analysis for complex engineered system design

2015 ◽  
Vol 29 (1) ◽  
pp. 93-108 ◽  
Author(s):  
Hoda Mehrpouyan ◽  
Brandon Haley ◽  
Andy Dong ◽  
Irem Y. Tumer ◽  
Christopher Hoyle
Keyword(s):  
Electrical Power ◽  
Nonlinear Dynamical System ◽  
Operating Environment ◽  
Electrical Power System ◽  
Nonlinear Dynamical ◽  
Complex Engineered Systems ◽  
Failure Propagation ◽  
Engineered Systems ◽  
Mean Time

AbstractResilience 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.

Resilient Design of Complex Engineered Systems

2013 ◽  
Author(s):  
Hoda Mehrpouyan ◽  
Brandon Haley ◽  
Andy Dong ◽  
Irem Y. Tumer ◽  
Chris Hoyle
Keyword(s):  
Complex Network ◽  
Adjacency Matrix ◽  
Laplacian Matrix ◽  
Algebraic Connectivity ◽  
Graph Spectra ◽  
Complex Engineered Systems ◽  
Resilient Design ◽  
Spectra Properties ◽  
Key Driver ◽  
Engineered Systems

This paper presents a complex network and graph spectral approach to calculate the resiliency of complex engineered systems. Resiliency is a key driver in how systems are developed to operate in an unexpected operating environment, and how systems change and respond to the environments in which they operate. This paper deduces resiliency properties of complex engineered systems based on graph spectra calculated from their adjacency matrix representations, which describes the physical connections between components in a complex engineered systems. In conjunction with the adjacency matrix, the degree and Laplacian matrices also have eigenvalue and eigenspectrum properties that can be used to calculate the resiliency of the complex engineered system. One such property of the Laplacian matrix is the algebraic connectivity. The algebraic connectivity is defined as the second smallest eigenvalue of the Laplacian matrix and is proven to be directly related to the resiliency of a complex network. Our motivation in the present work is to calculate the algebraic connectivity and other graph spectra properties to predict the resiliency of the system under design.

Resilience-Driven System Design of Complex Engineered Systems

2011 ◽  
Vol 133 (10) ◽  
Author(s):  
Byeng D. Youn ◽  
Chao Hu ◽  
Pingfeng Wang
Keyword(s):  
Adverse Events ◽  
System Design ◽  
System Reliability ◽  
Design Problem ◽  
Detailed Design ◽  
Bottom Level ◽  
Complex Engineered Systems ◽  
Driven System ◽  
Level Design ◽  
Engineered Systems

Most engineered systems are designed with a passive and fixed design capacity and, therefore, may become unreliable in the presence of adverse events. Currently, most engineered systems are designed with system redundancies to ensure required system reliability under adverse events. However, a high level of system redundancy increases a system’s life-cycle cost (LCC). Recently, proactive maintenance decisions have been enabled through the development of prognostics and health management (PHM) methods that detect, diagnose, and predict the effects of adverse events. Capitalizing on PHM technology at an early design stage can transform passively reliable (or vulnerable) systems into adaptively reliable (or resilient) systems while considerably reducing their LCC. In this paper, we propose a resilience-driven system design (RDSD) framework with the goal of designing complex engineered systems with resilience characteristics. This design framework is composed of three hierarchical tasks: (i) the resilience allocation problem (RAP) as a top-level design problem to define a resilience measure as a function of reliability and PHM efficiency in an engineering context, (ii) the system reliability-based design optimization (RBDO) as the first bottom-level design problem for the detailed design of components, and (iii) the system PHM design as the second bottom-level design problem for the detailed design of PHM units. The proposed RDSD framework is demonstrated using a simplified aircraft control actuator design problem resulting in a highly resilient actuator with optimized reliability, PHM efficiency and redundancy for the given parameter settings.

Safety-Informed Design: Using Cluster Analysis to Elicit Hazardous Emergent Failure Behavior in Complex Systems

2015 ◽  
Author(s):  
Matthew G. McIntire ◽  
Christopher Hoyle ◽  
Irem Y. Tumer ◽  
David C. Jensen
Keyword(s):  
Complex System ◽  
Hazard Analysis ◽  
Electrical Power ◽  
Functional Model ◽  
Hazard Identification ◽  
Emergent Behavior ◽  
Failure Behavior ◽  
Large Set ◽  
Electrical Power System ◽  
Engineered Systems

Identifying failure paths and potentially hazardous scenarios resulting from component faults and interactions is a challenge in the early design process. The inherent complexity present in large engineered systems leads to non-obvious emergent behavior, which may result in unforeseen hazards. Current hazard analysis techniques either focus on small slices of failure scenarios (fault trees and event trees), or lists of known hazards in the domain (hazard identification). Early in the design of a complex system, engineers may represent their system as a functional model. A function failure reasoning tool can then exhaustively simulate qualitative failure scenarios. Some scenarios will be identified as hazardous by hazard rules specified by the engineer, but the goal is to identify scenarios representing unknown hazards. A clustering method is applied repetitively to the large set of failure propagation results. Then, an algorithm identifies the scenario most likely to be hazardous, and presents it to the engineer. After viewing the scenario and judging its safety, the engineer may have insight to produce additional rules. The collaborative process of computer rating and human judgment will identify previously unknown hazards. The feasibility of this methodology is being tested on a relatively simple functional model of an electrical power system. Related work applying function failure reasoning to a team of robotic rovers will provide data from a more complex system.

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