A Comparison of Network-Based Metrics of Behavioral Degradation in Complex Engineered Systems

2016 ◽  
Vol 138 (12) ◽  
Author(s):  
Brandon M. Haley ◽  
Andy Dong ◽  
Irem Y. Tumer

It has been assumed, but not yet tested, that the topological disintegration of networks is relatable to degradations in complex engineered system behavior and that extant network metrics are capable of capturing these degradations. This paper tests three commonly used network metrics used to quantify the topological robustness of networks for their ability to characterize the degree of failure in engineered systems: average shortest path length, network diameter, and a robustness coefficient. A behavioral network of a complex engineered system is subjected to “attack” to simulate potential failures to the system. Average shortest path length and the robustness coefficient showed topological disintegration patterns which differed between nominal and failed cases, regardless of failure implementation location. The network diameter metric is not sufficiently dependent on local cluster topology to show changes in topology with edge removal failure strategies. The results show that topological metrics from the field of complex networks are applicable to complex engineered systems when they account for both local and global topological changes.

Author(s):  
Brandon M. Haley ◽  
Andy Dong ◽  
Irem Y. Tumer

This paper presents a new methodology for modeling complex engineered systems using complex networks for failure analysis. Many existing network-based modeling approaches for complex engineered systems “abstract away” the functional details to focus on the topological configuration of the system and thus do not provide adequate insight into system behavior. To model failures more adequately, we present two types of network representations of a complex engineered system: a uni-partite architectural network and a weighted bi-partite behavioral network. Whereas the architectural network describes physical inter-connectivity, the behavioral network represents the interaction between functions and variables in mathematical models of the system and its constituent components. The levels of abstraction for nodes in both network types affords the evaluation of failures involving morphology or behavior, respectively. The approach is shown with respect to a drivetrain model. Architectural and behavioral networks are compared with respect to the types of faults that can be described. We conclude with considerations that should be employed when modeling complex engineered systems as networks for the purpose of failure analysis.


2018 ◽  
Vol 4 ◽  
Author(s):  
Hannah S. Walsh ◽  
Andy Dong ◽  
Irem Y. Tumer

Recent advances in early stage failure analysis approaches have introduced behavioral network analysis (BNA), which applies a network-based model of a complex engineered system to detect the system-level effect of ‘local’ failures of design variables and parameters. Previous work has shown that changes in microscale network metrics can signify system-level performance degradation. This article introduces a new insight into the influence of the community structure of the behavioral network on the failure tolerance of the system through the role of bridging nodes. Bridging nodes connect a community of nodes in a system to one or more nodes or communities outside of the community. In a study of forty systems, it is found that bridging nodes, under attack, are associated with significantly larger system-level behavioral degradation than non-bridging nodes. This finding indicates that the modularity of the behavioral network could be key to understanding the failure tolerance of the system and that parameters associated with bridging nodes between modules could play a vital role in system degradation.


2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Douglas Guilbeault ◽  
Damon Centola

AbstractThe standard measure of distance in social networks – average shortest path length – assumes a model of “simple” contagion, in which people only need exposure to influence from one peer to adopt the contagion. However, many social phenomena are “complex” contagions, for which people need exposure to multiple peers before they adopt. Here, we show that the classical measure of path length fails to define network connectedness and node centrality for complex contagions. Centrality measures and seeding strategies based on the classical definition of path length frequently misidentify the network features that are most effective for spreading complex contagions. To address these issues, we derive measures of complex path length and complex centrality, which significantly improve the capacity to identify the network structures and central individuals best suited for spreading complex contagions. We validate our theory using empirical data on the spread of a microfinance program in 43 rural Indian villages.


Author(s):  
Frank H. Johnson ◽  
DeWitt William E.

Analytical Tools, Like Fault Tree Analysis, Have A Proven Track Record In The Aviation And Nuclear Industries. A Positive Tree Is Used To Insure That A Complex Engineered System Operates Correctly. A Negative Tree (Or Fault Tree) Is Used To Investigate Failures Of Complex Engineered Systems. Boeings Use Of Fault Tree Analysis To Investigate The Apollo Launch Pad Fire In 1967 Brought National Attention To The Technique. The 2002 Edition Of Nfpa 921, Guide For Fire And Explosion Investigations, Contains A New Chapter Entitled Failure Analysis And Analytical Tools. That Chapter Addresses Fault Tree Analysis With Respect To Fire And Explosion Investigation. This Paper Will Review The Fundamentals Of Fault Tree Analysis, List Recent Peer Reviewed Papers About The Forensic Engineering Use Of Fault Tree Analysis, Present A Relevant Forensic Engineering Case Study, And Conclude With The Results Of A Recent University Study On The Subject.


2020 ◽  
Vol 9 (2) ◽  
pp. 132-161 ◽  
Author(s):  
Ranjan Kumar ◽  
Sripati Jha ◽  
Ramayan Singh

The authors present a new algorithm for solving the shortest path problem (SPP) in a mixed fuzzy environment. With this algorithm, the authors can solve the problems with different sets of fuzzy numbers e.g., normal, trapezoidal, triangular, and LR-flat fuzzy membership functions. Moreover, the authors can solve the fuzzy shortest path problem (FSPP) with two different membership functions such as normal and a fuzzy membership function under real-life situations. The transformation of the fuzzy linear programming (FLP) model into a crisp linear programming model by using a score function is also investigated. Furthermore, the shortcomings of some existing methods are discussed and compared with the algorithm. The objective of the proposed method is to find the fuzzy shortest path (FSP) for the given network; however, this is also capable of predicting the fuzzy shortest path length (FSPL) and crisp shortest path length (CSPL). Finally, some numerical experiments are given to show the effectiveness and robustness of the new model. Numerical results show that this method is superior to the existing methods.


2014 ◽  
Vol 4 (4) ◽  
pp. 36-54 ◽  
Author(s):  
António Leitão ◽  
Adriano Vinhas ◽  
Penousal Machado ◽  
Francisco Câmara Pereira

Inverse Combinatorial Optimization has become a relevant research subject over the past decades. In graph theory, the Inverse Shortest Path Length problem becomes relevant when people don't have access to the real cost of the arcs and want to infer their value so that the system has a specific outcome, such as one or more shortest paths between nodes. Several approaches have been proposed to tackle this problem, relying on different methods, and several applications have been suggested. This study explores an innovative evolutionary approach relying on a genetic algorithm. Two scenarios and corresponding representations are presented and experiments are conducted to test how they react to different graph characteristics and parameters. Their behaviour and differences are thoroughly discussed. The outcome supports that evolutionary algorithms may be a viable venue to tackle Inverse Shortest Path problems.


2010 ◽  
Vol 132 (12) ◽  
Author(s):  
Christina L. Bloebaum ◽  
Anna-Maria R. McGowan

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