Research Papers: Design Automation

Resilience Modeling and Quantification for Engineered Systems Using Bayesian Networks

[+] Author and Article Information
Nita Yodo

Department of Industrial and
Manufacturing Engineering,
Wichita State University,
Wichita, KS 67260

Pingfeng Wang

Department of Industrial and
Manufacturing Engineering,
Wichita State University,
Wichita, KS 67260
e-mail: pingfeng.wang@wichita.edu

1Corresponding author.

Contributed by the Design Automation Committee of ASME for publication in the JOURNAL OF MECHANICAL DESIGN. Manuscript received June 18, 2015; final manuscript received December 17, 2015; published online January 20, 2016. Assoc. Editor: Xiaoping Du.

J. Mech. Des 138(3), 031404 (Jan 20, 2016) (12 pages) Paper No: MD-15-1428; doi: 10.1115/1.4032399 History: Received June 18, 2015; Revised December 17, 2015

The concept of engineering resilience has received a prevalent attention from academia as well as industry because it contributes a new means of thinking about how to withstand against disruptions and recover properly. Although the concept of resilience was scholarly explored in diverse disciplines, there are only few which focus on how to quantitatively measure the engineering resilience. This paper is dedicated to explore the gap between quantitative and qualitative assessment of engineering resilience in the domain of designing engineered systems in industrial applications. A conceptual framework is first proposed for modeling engineering resilience, and then Bayesian network (BN) is employed as a quantitative tool for the assessment and analysis of the resilience for engineered systems. Two industrial-based case studies, supply chain and production process, are employed to demonstrate the proposed approach. The proposed resilience quantification and analysis approach using BNs would empower system designers to have a better grasp of the weakness and strength of their own systems against system disruptions induced by adverse failure events.

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Fig. 1

Description of four transition states over the time with respect to the system performance function

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Fig. 2

A sample of BN with eight variables

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Fig. 3

A conceptual scheme of resilience for engineering systems

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Fig. 6

Supply chain network of electric motor

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Fig. 7

BN for electric motor supply chain

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Fig. 4

Main parts of an electric motor

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Fig. 5

Assembly structure of electric motor

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Fig. 8

Relation among nodes X4, X7, and X13

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Fig. 9

Comparison between scenario 7 (a) and scenario 10 (b) in resilience quantification

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Fig. 10

Comparison between scenario 14 (a) and scenario 16 (b) in resilience quantification

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Fig. 11

VSM for composite production process

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Fig. 12

BN for composite production process

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Fig. 13

Resilience assessment with and without restoration actions at baseline: (a) case 1—baseline without restoration actions and (b) case 5—baseline with restoration actions




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