Research Papers

Nested Multistate Design for Maximizing Probabilistic Performance in Persistent Observation Campaigns

[+] Author and Article Information
Jeremy S. Agte

Department of Aeronautics and Astronautics,
Air Force Institute of Technology, Dayton, OH 45433
e-mail: jeremy.agte@afit.edu

Nicholas K. Borer

Newport News, VA 23606
e-mail: nick_borer@yahoo.com

1Corresponding author.

Manuscript received March 3, 2015; final manuscript received May 25, 2015; published online November 20, 2015. Assoc. Editor: Ioannis Kougioumtzoglou.

ASME J. Risk Uncertainty Part B 2(1), 011006 (Nov 20, 2015) (9 pages) Paper No: RISK-15-1033; doi: 10.1115/1.4030948 History: Received March 03, 2015; Accepted July 02, 2015

The paper presents a nested multistate methodology for the design of mechanical systems (e.g., a fleet of vehicles) involved in extended campaigns of persistent surveillance. It uses multidisciplinary systems analysis and behavioral-Markov modeling to account for stochastic metrics such as reliability and availability across multiple levels of system performance. The effects of probabilistic failure states at the vehicle level are propagated to mission operations at the campaign level by nesting various layers of Markov and estimated-Markov models. A key attribute is that the designer can then quantify the impact of physical changes in the vehicle, even those physical changes not related to component failure rates, on the predicted chance of maintaining campaign operations above a particular success threshold. The methodology is demonstrated on the design of an unmanned aircraft for an ice surveillance mission requiring omnipresence over Antarctica. Probabilistic results are verified with Monte Carlo analysis and show that even aircraft design parameters not directly related to component failure rates have a significant impact on the number of aircraft lost and missions aborted over the course of the campaign.

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Fig. 1

Markov chain formulation for a generic three-element system. Each state is characterized by performance, GKs, and the probability, PKs, that the system finds itself in that particular state of performance at time t. The failure levels (FL) indicate the total number of failures the system has experienced within the corresponding level’s set of system states. λ are component failure rates.

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Fig. 2

Nested reliability analysis procedure

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Fig. 3

VMM—vehicle component failure rates are λA = failure rate of component A, 1/500  h; λB = failure rate of component B, 1/500  h; λC = failure rate of component C, 1/200  h; λD = failure rate of component D, 1/300  h

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Fig. 4

SEM—sortie equivalent states are N = nominal; D = degraded; A = sortie abort; L = loss of vehicle

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Fig. 5

CEM—CEM rates come from SEM: λNDCEM=λNDSEM=1/198.2  h; λDFCEM=λNDASEM+λNDLSEM=1/141.7  h; λNFCEM=2  ptλNASEM+λNLSEM=1/1566  h; μDNCEM=f(MTTF); μFNCEM=1/346  h

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Fig. 6

Main effect of maximum engine power on campaign availability

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Fig. 7

Main effect of wing sweep on total vehicles lost

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Fig. 8

Tradespace of life-cycle cost and campaign availability showing effect of manipulation of component failure rates for geometry optimized for nominal performance (shaded diamonds), manipulation of design variables with fixed component failure rates (open diamonds), and concurrent multistate manipulation of both parameters (black diamonds)

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Fig. 9

Life-cycle cost comparison of baseline and multistate solutions. Stratifications in data produced by varying x are due to the fact that design geometry can only affect availability through system performance, thus changes occur through step transitions as system performance crosses a threshold (e.g., from abort to degraded). Component failure rates, on the other hand, affect availability through transition probabilities in a continuous fashion, thus data are not stratified.




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