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Research Papers: Design Automation

Resilience Assessment Based on Time-Dependent System Reliability Analysis

[+] Author and Article Information
Zhen Hu

Department of Civil and
Environmental Engineering,
Vanderbilt University,
279 Jacobs Hall,
Nashville, TN 37235
e-mail: zhen.hu@vanderbilt.edu

Sankaran Mahadevan

Professor
Department of Civil and
Environmental Engineering,
Vanderbilt University,
272 Jacobs Hall,
Nashville, TN 37235
e-mail: sankaran.mahadevan@vanderbilt.edu

1Corresponding author.

Contributed by the Design Automation Committee of ASME for publication in the JOURNAL OF MECHANICAL DESIGN. Manuscript received February 22, 2016; final manuscript received June 23, 2016; published online September 12, 2016. Assoc. Editor: Mian Li.

J. Mech. Des 138(11), 111404 (Sep 12, 2016) Paper No: MD-16-1140; doi: 10.1115/1.4034109 History: Received February 22, 2016; Revised June 23, 2016

Significant efforts have been recently devoted to the qualitative and quantitative evaluation of resilience in engineering systems. Current resilience evaluation methods, however, have mainly focused on business supply chains and civil infrastructure, and need to be extended for application in engineering design. A new resilience metric is proposed in this paper for the design of mechanical systems to bridge this gap, by investigating the effects of recovery activity and system failure paths on system resilience. The defined resilience metric is connected to design through time-dependent system reliability analysis. This connection enables us to design a system for a specific resilience target in the design stage. Since computationally expensive computer simulations are usually used in design, a surrogate modeling method is developed to efficiently perform time-dependent system reliability analysis. Based on the time-dependent system reliability analysis, dominant system failure paths are enumerated and then the system resilience is estimated. The connection between the proposed resilience assessment method and design is explored through sensitivity analysis and component importance measure (CIM). Two numerical examples are used to illustrate the effectiveness of the proposed resilience assessment method.

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References

Plodinec, M. J. , 2009, “ Definitions of Resilience: An Analysis,” Oak Ridge: Community and Regional Resilience Institute (CARRI), Report No. accessed Dec. 20, 2015, http://www.resilientus.org/wp-content/uploads/2013/08/definitions-of-community-resilience.pdf
Cumming, G. S. , 2011, “ Spatial Resilience: Integrating Landscape Ecology, Resilience, and Sustainability,” Landscape Ecol., 26(7), pp. 899–909. [CrossRef]
Norris, F. H. , Stevens, S. P. , Pfefferbaum, B. , Wyche, K. F. , and Pfefferbaum, R. L. , 2008, “ Community Resilience as a Metaphor, Theory, Set of Capacities, and Strategy for Disaster Readiness,” Am. J. Commun. Psychol., 41(1–2), pp. 127–150. [CrossRef]
Plummer, R. , and Armitage, D. , 2007, “ A Resilience-Based Framework for Evaluating Adaptive Co-management: Linking Ecology, Economics and Society in a Complex World,” Ecol. Econ., 61(1), pp. 62–74. [CrossRef]
ASME-ITI, 2009, All Hazards Risk and Resilience–Prioritizing Critical Infrastructure Using the RAMCAP Plus SM Approach, ASME Innovative Technology Institute, Washington, DC. http://files.asme.org/ASMEITI/RAMCAP/17978.pdf
Ouyang, M. , and Wang, Z. , 2015, “ Resilience Assessment of Interdependent Infrastructure Systems: With a Focus on Joint Restoration Modeling and Analysis,” Reliab. Eng. Syst. Saf., 141, pp. 74–82. [CrossRef]
Ayyub, B. M. , 2014, “ Systems Resilience for Multihazard Environments: Definition, Metrics, and Valuation for Decision Making,” Risk Anal., 34(2), pp. 340–355. [CrossRef] [PubMed]
Reed, D. A. , Kapur, K. C. , and Christie, R. D. , 2009, “ Methodology for Assessing the Resilience of Networked Infrastructure,” IEEE Syst. J., 3(2), pp. 174–180. [CrossRef]
Hosseini, S. , Barker, K. , and Ramirez-Marquez, J. E. , 2016, “ A Review of Definitions and Measures of System Resilience,” Reliab. Eng. Syst. Saf., 145, pp. 47–61. [CrossRef]
Hosseini, S. , Yodo, N. , and Wang, P. , “ Resilience Modeling and Quantification for Design of Complex Engineered Systems Using Bayesian Networks,” ASME Paper No. DETC2014-34558.
Yodo, N. , and Wang, P. , 2016, “ Resilience Modeling and Quantification for Engineered Systems Using Bayesian Networks,” ASME J. Mech. Des., 138(3), p. 031404. [CrossRef]
Panteli, M. , and Mancarella, P. , 2015, “ Modeling and Evaluating the Resilience of Critical Electrical Power Infrastructure to Extreme Weather Events,” IEEE Syst. J., PP(99), pp. 1–10. [CrossRef]
Baroud, H. , Barker, K. , and Ramirez-Marquez, J. E. , 2014, “ Importance Measures for Inland Waterway Network Resilience,” Transp. Res., Part E, 62, pp. 55–67. [CrossRef]
Barker, K. , Ramirez-Marquez, J. E. , and Rocco, C. M. , 2013, “ Resilience-Based Network Component Importance Measures,” Reliab. Eng. Syst. Saf., 117, pp. 89–97. [CrossRef]
Spiegler, V. L. , Naim, M. M. , and Wikner, J. , 2012, “ A Control Engineering Approach to the Assessment of Supply Chain Resilience,” Int. J. Prod. Res., 50(21), pp. 6162–6187. [CrossRef]
Youn, B. D. , Hu, C. , and Wang, P. , 2011, “ Resilience-Driven System Design of Complex Engineered Systems,” ASME J. Mech. Des., 133(10), p. 101011. [CrossRef]
Mehrpouyan, H. , Haley, B. , Dong, A. , Tumer, I . Y. , and Hoyle, C. , 2015, “ Resiliency Analysis for Complex Engineered System Design,” Artif. Intell. Eng. Des., Anal. Manuf., 29(1), pp. 93–108. [CrossRef]
Wang, J. , and Li, M. , 2015, “ Redundancy Allocation for Reliability Design of Engineering Systems With Failure Interactions,” ASME J. Mech. Des., 137(3), p. 031403. [CrossRef]
Wang, J. , and Li, M. , 2015, “ Redundancy Allocation Optimization for Multistate Systems With Failure Interactions Using Semi-Markov Process,” ASME J. Mech. Des., 137(10), p. 101403. [CrossRef]
Hu, Z. , and Mahadevan, S. , 2015, “ Time-Dependent System Reliability Analysis Using Random Field Discretization,” ASME J. Mech. Des., 137(10), p. 101404. [CrossRef]
Mourelatos, Z. P. , Majcher, M. , Pandey, V. , and Baseski, I. , 2015, “ Time-Dependent Reliability Analysis Using the Total Probability Theorem,” ASME J. Mech. Des., 137(3), p. 031405. [CrossRef]
Jeon, B. C. , Jung, J. H. , Youn, B. D. , Kim, Y.-W. , and Bae, Y.-C. , 2015, “ Datum Unit Optimization for Robustness of a Journal Bearing Diagnosis System,” Int. J. Precis. Eng. Manuf., 16(11), pp. 2411–2425. [CrossRef]
Hu, C. , Youn, B. D. , Wang, P. , and Yoon, J. T. , 2012, “ Ensemble of Data-Driven Prognostic Algorithms for Robust Prediction of Remaining Useful Life,” Reliab. Eng. Syst. Saf., 103, pp. 120–135. [CrossRef]
Hu, Z. , and Du, X. , 2015, “ First Order Reliability Method for Time-Variant Problems Using Series Expansions,” Struct. Multidiscip. Optim., 51(1), pp. 1–21. [CrossRef]
Hu, Z. , and Mahadevan, S. , “ Accelerated Life Testing (ALT) Design Based on Computational Reliability Analysis,” Qual. Reliab. Eng. Int. (in press).
Hu, Z. , and Du, X. , 2013, “ Time-Dependent Reliability Analysis With Joint Upcrossing Rates,” Struct. Multidiscip. Optim., 48(5), pp. 893–907. [CrossRef]
Wang, Z. , and Wang, P. , 2012, “ A Nested Extreme Response Surface Approach for Time-Dependent Reliability-Based Design Optimization,” ASME J. Mech. Des., 134(12), p. 121007. [CrossRef]
Hu, Z. , and Du, X. , 2015, “ Mixed Efficient Global Optimization for Time-Dependent Reliability Analysis,” ASME J. Mech. Des., 137(5), p. 051401. [CrossRef]
Singh, A. , Mourelatos, Z. P. , and Li, J. , 2010, “ Design for Lifecycle Cost Using Time-Dependent Reliability,” ASME J. Mech. Des., 132(9), p. 091008. [CrossRef]
Bruneau, M. , Chang, S. E. , Eguchi, R. T. , Lee, G. C. , O'Rourke, T. D. , Reinhorn, A. M. , Shinozuka, M. , Tierney, K. , Wallace, W. A. , and von Winterfeldt, D. , 2003, “ A Framework to Quantitatively Assess and Enhance the Seismic Resilience of Communities,” Earthquake Spectra, 19(4), pp. 733–752. [CrossRef]
Song, J. , and Der Kiureghian, A. , 2006, “ Joint First-Passage Probability and Reliability of Systems Under Stochastic Excitation,” J. Eng. Mech., 132(1), pp. 65–77. [CrossRef]
Hu, Z. , Zhu, Z. , and Du, X. , 2015, “ Time-Dependent Reliability Analysis for Bivariate Responses,” ASME Paper No. IMECE2015-53441.
Hu, Z. , and Mahadevan, S. , 2016, “ A Single-Loop Kriging Surrogate Modeling for Time-Dependent Reliability Analysis,” ASME J. Mech. Des., 138(6), p. 061406. [CrossRef]
Mahadevan, S. , and Dey, A. , 1997, “ Adaptive Monte Carlo Simulation for Time-Variant Reliability Analysis of Brittle Structures,” AIAA J., 35(2), pp. 321–326. [CrossRef]
Melchers, R. E. , 1999, Structural Reliability Analysis and Prediction, Wiley, New York.
Song, J. , and Der Kiureghian, A. , 2003, “ Bounds on System Reliability by Linear Programming,” ASME J. Eng. Mech., 129(6), pp. 627–636. [CrossRef]
Bichon, B. J. , McFarland, J. M. , and Mahadevan, S. , 2011, “ Efficient Surrogate Models for Reliability Analysis of Systems With Multiple Failure Modes,” Reliab. Eng. Syst. Saf., 96(10), pp. 1386–1395. [CrossRef]
Fauriat, W. , and Gayton, N. , 2014, “ AK-SYS: An Adaptation of the AK-MCS Method for System Reliability,” Reliab. Eng. Syst. Saf., 123, pp. 137–144. [CrossRef]
Rasmussen, C. E. , 2006, Gaussian Processes for Machine Learning, The MIT Press, Cambridge, MA.
Wang, P. , Hu, C. , and Youn, B. D. , 2011, “ A Generalized Complementary Intersection Method (GCIM) for System Reliability Analysis,” ASME J. Mech. Des., 133(7), p. 071003. [CrossRef]
Lee, I. , Choi, K. , and Zhao, L. , 2011, “ Sampling-Based RBDO Using the Stochastic Sensitivity Analysis and Dynamic Kriging Method,” Struct. Multidiscip. Optim., 44(3), pp. 299–317. [CrossRef]
Rahman, S. , 2009, “ Stochastic Sensitivity Analysis by Dimensional Decomposition and Score Functions,” Probab. Eng. Mech., 24(3), pp. 278–287. [CrossRef]
Kuo, W. , and Zhu, X. , 2012, “ Some Recent Advances on Importance Measures in Reliability,” IEEE Trans. Reliab., 61(2), pp. 344–360. [CrossRef]
Li, C. , and Mahadevan, S. , 2016, “ Role of Calibration, Validation, and Relevance in Multi-Level Uncertainty Integration,” Reliab. Eng. Syst. Saf., 148, pp. 32–43. [CrossRef]

Figures

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Fig. 1

A generalized representation of system resilience

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Fig. 2

Resilience considering different failure and recovery paths

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Fig. 3

Illustration of system QoI with failure and recovery

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Fig. 4

A system with brittle failure events

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Fig. 5

First-passage point of a realization for given X=x

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Fig. 6

An example of a combined system

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Fig. 8

Comparison of time-dependent system failure probability

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Fig. 9

Resilience of the roller clutch over 20 years

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Fig. 10

Resilience sensitivity of roller clutch

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Fig. 11

Resilience CIM analysis of roller clutch

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Fig. 12

A cantilever beam-bar system

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Fig. 13

Modified RBD of the cantilever beam-bar system (i|j stands for the failure of the ith component given that the jth component fails in Eqs. (61)(65)): (a) failure sequences and (b) RBD

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Fig. 14

Resilience of the cantilever beam-bar example

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Fig. 15

CIM analysis of the cantilever beam-bar example

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