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Research Papers: Design Automation

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

[+] Author and Article Information
Brandon M. Haley

Complex Engineered Systems
Design Laboratory,
School of Mechanical, Industrial, and
Manufacturing Engineering,
Oregon State University,
Corvallis, OR 97331
e-mail: haleybr@onid.orst.edu

Andy Dong

Faculty of Engineering and
Information Technologies,
University of Sydney,
Sydney, New South Wales 2006, Australia
e-mail: andy.dong@sydney.edu.au

Irem Y. Tumer

Complex Engineered Systems
Design Laboratory,
School of Mechanical, Industrial, and
Manufacturing Engineering,
Oregon State University,
Corvallis, OR 97331
e-mail: irem.tumer@oregonstate.edu

Contributed by the Design Automation Committee of ASME for publication in the JOURNAL OF MECHANICAL DESIGN. Manuscript received June 2, 2016; final manuscript received July 26, 2016; published online September 19, 2016. Assoc. Editor: Xiaoping Du.

J. Mech. Des 138(12), 121405 (Sep 19, 2016) (11 pages) Paper No: MD-16-1414; doi: 10.1115/1.4034402 History: Received June 02, 2016; Revised July 26, 2016

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.

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References

Figures

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Fig. 1

Bipartite network. Two node types, three nodes within type 1 and four nodes within type 2.

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Fig. 2

Bipartite adjacency matrix A for network in Fig. 1

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Fig. 3

Typical network profile [39]

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Fig. 4

Idealized network profile [39]

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Fig. 5

Drivetrain model constructed in openmodelica [14]

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Fig. 6

Simulation results of a drivetrain: nominal (solid/blue line) and slipping clutch (dashed/red line)

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Fig. 7

Bipartite network graph of drivetrain

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Fig. 8

Adjacency matrix for nominally operating drivetrain

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Fig. 9

Adjacency matrix for failed drivetrain clutch, FV2=0.5

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Fig. 10

Small-scale drivetrain network topology results for failed mu in clutch

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Fig. 11

Small-scale drivetrain network topology results for the independent failure of all nodes

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Fig. 12

DARPA AVM drivetrain with no controls system model

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Fig. 13

No controls drivetrain network topology results for a single failed variable

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Fig. 14

No controls drivetrain network topology results for the independent failure of all nodes

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