This article presents an integrated multistate method for the early-phase design of inherently robust systems; namely, those capable, as a prima facie quality, of maintaining adequate performance in the face of probabilistic system events or failures. The methodology merges integrated multidisciplinary analysis techniques for system design with behavioral-Markov analysis methods used to define probabilistic metrics such as reliability and availability. The result is a multistate approach that concurrently manipulates design variables and component failure rates to better identify key features for an inherently robust system. This methodology is demonstrated on the design of a long-endurance unmanned aerial vehicle for a three-month ice surveillance mission over Antarctica. The vehicle is designed using the multistate methodology and then compared to a baseline design created for the best performance under nominal conditions. Results demonstrated an improvement of 10–11% in system availability over this period with minimal impacts on cost or performance.

References

References
1.
Northrop-Grumman, 2012, “
RQ-4 Block 20 Global Hawk Data Sheet
,” http://www.as.northropgrumman.com/products/ghrq4a/assets/GH_Brochure.pdf, accessed June 2012.
2.
Gilmore
,
M. J.
, 2011, “
RQ-4B Global Hawk Block 30 Operational Test and Evaluation Report
,” Department of Defense, http://pogoarchives.org/m/ns/pentagon-ot-and-e-eval-rq-4b-global-hawk-20110526.pdf, last accesssed September 2012.
3.
DARPA, 2009, “
Broad Agency Announcement (BAA) Vulture II
,” Defense Advanced Research Projects Agency TTO, Report No. DARPA-BAA-10-04.
4.
National Transportation Safety Board, 2006, “
Annual Review of Aircraft Accident Data—U.S. Air Carrier Operations Calendar Year 2006
,” Technical Report NTSB/ARC-10/01.
5.
Department of Defense, 2003, “
Unmanned Aerial Vehicle Reliability Study
,” http://www.uadrones.net/military/research/acrobat/0302.pdf, accessed June 2012.
6.
Agte
,
J.
,
de Weck
,
O.
,
Sobieski
,
J.
,
Arendsen
,
P.
,
Morris
,
A.
, and
Spieck
,
M.
, 2010, “
MDO: Assessment and Direction for Advancement—An Opinion of One International Group
,”
Struct. Multidiscip. Optim.
,
40
(
1
), pp.
17
33
.
7.
Saha
,
A.
, and
Ray
,
T.
, 2011,
“Practical Robust Design Optimization Using Evolutionary Algorithms
,”
J. Mech. Des.
,
133
(
10
), p.
101012
.
8.
Erfani
,
T.
, and
Utyuzhnikov
,
S.
, 2012, “
Control of Robust Design in Multiobjective Optimization Under Uncertainties
,”
J. Struct. Multidiscip. Optim.
,
2
(
45
), pp.
247
256
.
9.
Morris
,
J.
,
Ingham
,
M.
,
Mishkin
,
A.
,
Rasmussen
,
R.
, and
Starbird
,
T.
, 2006, “
Application of State Analysis and Goal-Based Operations to a MER Mission Scenario
,” AIAA International Conference on Space Operations, Rome, Italy, Paper No. AIAA 2006-5981.
10.
Braman
,
J.
,
Murray
,
R.
, and
Wagner
,
D.
, 2007, “
Safety Verification of a Fault Tolerant Reconfigurable Autonomous Goal-Based Robotic Control System
,” Proceedings of the 2007 IEEE/RSJ International Conference on Intelligent Robots and Systems.
11.
Ward
,
D. G.
, and
Monaco
,
J. F.
, 2005, “
System Identification for Retrofit Reconfigurable Control of an F/A-18 Aircraft
,”
J. Aircr.
,
42
(
1
), pp.
63
72
.
12.
Blackmore
,
L.
,
Ono
,
M.
,
Bektassov
,
A.
, and
Williams
,
B.
, 2010, “
A Probabilistic Particle-Control Approximation of Chance-Constrained Stochastic Predictive Control,”
IEEE Trans. Rob.
,
26
(
3
), pp.
502
517
.
13.
Barlow
,
R. E.
, and
Wu
,
A. S.
, 1978, “
Coherent Systems With Multi-State Components
,”
Math. Oper. Res.
,
3
(
4
), pp.
275
281
.
14.
Lisnianski
,
A.
, and
Levitin
,
G.
, 2003,
Multi-State System Reliability
, Vol. 6 (Series on Quality, Reliability & Engineering Statistics),
World Scientific
,
Singapore
.
15.
Taboada
,
H. A.
,
Espiritu
,
J. F.
, and
Coit
,
D. W.
, 2008, “
MOMS-GA: A Multi-Objective Multi-State Genetic Algorithm for System Reliability Optimization Design Problems
,”
IEEE Trans. Reliab.
,
24
(
1
), pp.
182
191
.
16.
Massim
,
Y.
,
Zeblah
,
A.
,
Meziane
,
R.
,
Benguediab
,
M.
, and
Ghouraf
,
A.
, 2005, “
Optimal Design and Reliability Evaluation of Multi-State Series-Parallel Power Systems
,”
Nonlinear Dyn.
,
40
(
4
), pp.
309
321
.
17.
Ushakov
,
I.
, 2000, “
The Method of Generalized Generating Sequences
,”
Eur. J. Oper. Res.
,
125
(
2
), pp.
675
688
.
18.
Smotherman
,
M.
, and
Zemoudeh
,
K.
, 1989, “
A Non-Homogeneous Markov Model for Phased-Mission Reliability Analysis
,”
IEEE Trans. Reliab.
,
38
(
5
), pp.
585
590
.
19.
Babcock
,
P. S.
, IV
, 1996, “
Channelization: the Two-Fault Tolerant Attitude Control Function for the Space Station Freedom
,”
IEEE Aerosp. Electron. Syst. Mag.
,
11
(
5
), pp.
9
22
.
20.
Borer
,
N. K.
,
Claypool
,
I. R.
,
Clark
,
W. D.
,
West
,
J. J.
,
Odegard
,
R. G.
,
Somervill
,
K. M.
, and
Suzuki
,
N. H.
, 2010, Model-Driven Development of Reliable Avionics Architectures for Lunar Surface Systems, 2010 IEEE Aerospace Conference.
21.
Allinger
,
D. F.
,
Babcock
,
P. S.
, and
LaPrad
,
R. F.
, May 1990, “
The Role of Time-Limited Dispatch Operation in Fault Tolerant Flight Critical Control Systems
,” AGARD Advisory Report No. 281.
22.
Siddiqi
,
A.
,
de Weck
,
O.
, and
Iagnemma
,
K.
, 2006, “
Reconfigurability in Planetary Surface Vehicles: Modeling Approaches and Case Study
,”
J. Br. Interplanetary Soc.
,
59
(
12
), pp.
450
460
.
23.
Haas
,
P. J.
, 2002,
Stochastic Petri Nets: Modelling, Stability, Simulation
,
Springer
,
New York, NY
.
24.
Dominguez-Garcia
,
A. D.
,
Kassakian
,
J. G.
,
Schindall
,
J. E.
, and
Zinchuk
,
J. J.
, 2008, “
An Integrated Methodology for the Dynamic Performance and Reliability Evaluation of Fault-Tolerant Systems
,”
J. Reliab. Eng. Syst. Saf.
,
93
(
11
), pp.
1628
1649
.
25.
Babcock
,
P. S.
, IV
,
Rosch
,
G.
, and
Zinchuk
,
J. J.
, 1991, “
An Automated Environment for Optimizing Fault-Tolerant Systems Designs
,”
Proceedings of the (1991) Annual Reliability and Maintainability Symposium (IEEE)
, pp.
360
367
.
26.
Howard
,
R. A.
, 2007,
Dynamic Probabilistic Systems: Volume I, Markov Models
,
Dover Publications
,
Mineola, NY
.
27.
Levitin
,
G.
, and
Lisnianski
,
A.
, 2001, “
A New Approach to Solving Problems of Multi-State System Reliability Optimization
,”
Qual. Reliab. Eng. Int.
,
17
(
2
), pp.
93
104
.
28.
Lisnianski
,
A.
,
Levitin
,
G.
,
Ben-Haim
,
H.
, and
Elmakis
,
D.
, 1996, “
Power System Structure Optimization Subject to Reliability Contraints
,”
Electr. Power Syst. Res.
,
39
(
2
), pp.
145
152
.
29.
Department of Defense, 1980, “
Military Standard: Procedures for Performing a Failure Mode, Effects and Criticality Analysis
,” Paper No. MIL-STD-1629A.
30.
Agte
,
J.
,
Borer
,
N.
, and
de Weck
,
O.
, 2012, “
Multistate Design Approach to the Analysis of Performance Robustness for a Twin-engine Aircraft
,”
J. Aircr.
,
49
(
3
), pp.
781
793
.
31.
NASA, 2012, “
Icebridge: An Airborne Mission for Earth’s Polar Ice
,” http://www.nasa.gov/mission_pages/icebridge/, accessed June 2012.
32.
Grasmeyer
,
J.
, 1998, “
Stability and Control Derivative Estimation and Engine-Out Analysis
,” Virginia Tech, Paper No. VPI-AOE-254.
33.
Roskam
,
J.
, 1971,
Methods for Estimating Stability and Control Derivatives of Conventional Subsonic Airplanes
,
Roskam Aviation and Engineering Corporation
,
Kansas
.
34.
Raymer
,
D. P.
, 1999,
Aircraft Design: A Conceptual Approach
,
3rd ed.
,
American Institute of Aeronautics and Astronautics
,
Reston, VA
.
35.
Moire, Inc., 2004, “
Cost and Business Model Analysis for Civilian UAV Missions
,” National Aeronautics and Space Administration, NASA.
36.
Agte
,
J.
, and
Cohen
,
K.
, 2008, “
First Order Effects of New Technology on a High Altitude Long Endurance (HALE) Unmanned Aerial Vehicle (UAV)
,” Proceedings of the 46th AIAA Aerospace Sciences Meeting and Exhibit.
37.
West
,
J.
,
Claypool
,
I.
,
Borer
,
N.
, and
Odegard
,
R.
, 2009, “
LSS Lunar Habitat Avionics Modeling Study - Task 3 Final Report, Reliability and Performance Implications of the Use of Commercial Grade Components
,” NASA/JSC Contract No. NNJ06HC37C, Draper Laboratory.
38.
Mettas
,
A.
, 2000, “
Reliability Allocation and Optimization for Complex Systems
,” Proceedings of the Annual IEEE Reliability and Maintainability Symposium.
39.
Nickol
,
C. L.
,
Guynn
,
M. D.
,
Kohout
,
L. L.
, and
Ozoroski
,
T. A.
, 2007,
High Altitude Long Endurance UAV Analysis of Alternatives and Technology Requirements Development
,” Paper No. NASA/TP-2007-214861.
40.
Kleijnen
,
J. P. C.
, 2010,
Design and Analysis of Simulation Experiments
,
Springer
,
New York, NY
.
41.
Drela
,
M.
, and
Youngren
,
H.
, 2012, “
Athena Vortex Lattice Solver. Massachusetts Institute of Technology
,” http://web.mit.edu/drela/Public/web/avl/, accessed June 2012.
42.
Tsach
,
S.
,
Peled
,
A.
,
Penn
,
D.
, and
Touitou
,
D.
, 2004, “
The CAPECON Program: Civil Applications and Economical Effectivity of Potential UAV Configurations
,” Proceedings of the 3rd AIAA Unmanned Unlimited Technical Conference, Workshop, and Exhibit.
You do not currently have access to this content.