Integrated Systems Health Management (ISHM) is an evolving technology used to detect, assess, and isolate faults in complex systems to improve safety. At the conceptual design level, system-level engineers must make decisions regarding the inclusion of ISHM and the extent and type of the sensing technologies used in various subsystems. In this paper, we propose an ISHM design tool to be used in conjunction with standard system modeling methods to help with the integration of ISHM into the system design process. The key to this analysis is the formulation of an objective function that explicitly quantifies the value derived by integrating the ISHM technology in various subsystems. Ultimately, to determine the best ISHM system configuration, an objective function is formulated, referred to as profit, which is expressed as the product of system availability and revenue per unit availability , minus the sum of cost of detection and cost of risk . The analysis is conducted at the system functional level appropriate for conceptual design using standard system functional modeling methods, and ISHM is allocated to the functional blocks using the ISHM design tool. The proposed method is demonstrated using a simplified aerospace system design problem resulting in a configuration of sensors, which optimizes the value of the ISHM system for the given input parameters. In this problem, profit was increased by 11%, inspection interval increased by a factor of 1.5 and cost of risk reduced by a factor of 2.4 over a system with no ISHM.
Issue Section:
Research Papers
Keywords:
aerospace engineering,
design engineering,
fault diagnosis,
large-scale systems,
maintenance engineering
Topics:
Design,
Failure,
Sensors,
Risk,
Inspection,
Probability,
Optimization,
Conceptual design,
Modeling
1.
Williams
, Z.
, 2006, “Benefits of IVHM: An Analytical Approach
,” 2006 IEEE Aerospace Conference Proceedings
, Big Sky MT, Mar. 4–11.2.
Kent
, R. M.
, and Murphy
, D. A.
, 2000, “Health Monitoring System Technology Assessments Cost Benefits Analysis
,” NASA Langley Research Center
, Technical Report No. NASA/CR-2000-209848.3.
Kacprzynski
, G. J.
, Roemer
, M. J.
, and Hess
, A. J.
, 2002, “Health Management System Design: Development, Simulation and Cost/Benefit Optimization
,” IEEE Aerospace Conf. Proc.
, Vol. 6
, pp. 3065
–3072
.4.
Mehr
, A. F.
, Tumer
, I.
, and Barszcz
, E.
, 2005, “Optimal Design of Integrated Systems Health Management (ISHM) for Improving the Safety of NASA’s Exploration Missions: A Multidisciplinary Design Approach
,” Sixth World Congress on Structural and Multidisciplinary Optimization
, Rio de Janeiro, May 30–Jun 3.5.
Datta
, K.
, Jize
, N.
, Maclise
, D.
, and Goggin
, D.
, 2004, “An IVHM Systems Analysis & Optimization Process
,” IEEE Aerospace Conf. Proc.
, 6
.6.
Padula
, S. L.
, and Kincaid
, R. K.
, 1999, “Optimization Strategies for Sensor and Actuator Placement
,” NASA Langley Research Center
, Technical Report No. NASA/TM-1999-209126.7.
Misra
, K. B.
, and Sharma
, U.
, 1991, “An Efficient Algorithm to Solve Integer-Programming Problems Arising in System-Reliability Design
,” IEEE Trans. Reliab.
0018-9529, 40
(1
), pp. 81
–90
.8.
Painton
, L.
, and Campbell
, J.
, 1995, “Genetic Algorithms in Optimization of System Reliability
,” IEEE Trans. Reliab.
0018-9529, 44
(2
), pp. 172
–178
.9.
Handlarski
, J.
, 1980, “Mathematical Analysis of Preventive Maintenance Schemes
,” J. Oper. Res. Soc.
0160-5682, 31
(3
), pp. 227
–237
.10.
Parkinson
, R. C.
, 1993, “System Costing of Risk in a Launch Vehicle
,” 44th International Astronautical Congress
, Graz, Austria, Oct. 16–22.11.
Parkinson
, R. C.
, 1998, “The Hidden Costs of Reliability and Failure in Launch Systems
,” Journal of Reducing Space Mission Cost
, 1
(3
), pp. 263
–273
.12.
Smith
, N.
, and Mahadevan
, S.
, 2003, “Probabilistic Methods for Aerospace System Conceptual Design
,” J. Spacecr. Rockets
0022-4650, 40
(3
), pp. 411
–418
.13.
Mehr
, A. F.
, and Tumer
, I. Y.
, 2006, “Risk-Based Decision-Making for Managing Resources During the Design of Complex Space Exploration Systems
,” ASME J. Mech. Des.
0161-8458, 128
, pp. 1014
–1022
.14.
Wickens
, T. D.
, 2002, Elementary Signal Detection Theory
, Oxford University Press
, New York
.15.
Swets
, J. A.
, 1986, “Indices of Discrimination or Diagnostic Accuracy: Their ROCs and Implied Models
,” Psychol. Bull.
0033-2909, 99
(1
), pp. 100
–117
.16.
Guratzsch
, R. F.
, and Mahadevan
, S.
, 2006, “Health Monitoring Sensor Placement Optimization Under Uncertainty
,” 11th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference
, Portsmouth, VA, Sept. 6–8.17.
Khan
, A.
, Ceglarek
, D.
, Shi
, J.
, Ni
, J.
, and Woo
, T. C.
, 1999, “Sensor Optimization for Fault Diagnosis in Single Fixture Systems: A Methodology
,” ASME J. Manuf. Sci. Eng.
1087-1357, 121
(1
), pp. 109
–117
.18.
Gao
, H.
, and Rose
, J. L.
, 2006, “Sensor Placement Optimization in Structural Health Monitoring Using Genetic and Evolutionary Algorithms
,” Proc. SPIE
0277-786X, 6174
, pp. 310
–321
.19.
Zhao
, F.
, Shin
, J.
, and Reich
, J.
, 2002, “Information-Driven Dynamic Sensor Collaboration for Tracking Applications
,” IEEE Signal Process. Mag.
1053-5888, 19
(2
), pp. 61
–72
.20.
Kirschman
, C. F.
, and Fadel
, G. M.
, 1998, “Classifying Functions for Mechanical Design
,” ASME J. Mech. Des.
0161-8458, 120
(3
), pp. 475
–482
.21.
Stone
, R. B.
, and Wood
, K. L.
, 2000, “Development of a Functional Basis for Design
,” ASME J. Mech. Des.
0161-8458, 122
(4
), pp. 359
–370
.22.
Cowan
, F. S.
, Allen
, J. K.
, and Mistree
, F.
, 2006, “Functional Modelling in Engineering Design: A Perspectival Approach Featuring Living Systems Theory
,” Syst. Res. Behav. Sci.
1092-7026, 23
, pp. 365
–381
.23.
Pahl
, G. G.
, and Beitz
, W.
, 1996, Engineering Design: A Systematic Approach
, Springer
, Berlin
.24.
Hunt
, J. E.
, Pugh
, D. R.
, and Price
, C. J.
, 1995, “Failure Mode Effects Analysis: A Practical Application of Functional Modeling
,” Eng. Applic. Artif. Intell.
0952-1976, 9
(1
), pp. 33
–44
.25.
Stone
, R. B.
, Tumer
, I. Y.
, and Van Wie
, M.
, 2005, “The Function-Failure Design Method
,” ASME J. Mech. Des.
0161-8458, 127
(3
), pp. 397
–407
.26.
Eubanks
, C. F.
, Kmenta
, S.
, and Ishii
, K.
, 1997, “Advanced Failure Modes and Effects Analysis Using Behavior Modeling
,” ASME Design Engineering Technical Conferences
, Sacramento, CA, Sept. 14–17.27.
Kmenta
, S.
, and Ishii
, K.
, 1998, “Advanced FMEA Using Meta Behavior Modeling for Concurrent Design of Products and Controls
,” ASME Design Engineering Technical Conferences
, Atlanta, GA, Sept. 13–16.28.
Montgomery
, T. A.
, Pugh
, D. R.
, Leedham
, S. T.
, Twitchett
, S. R.
, Co
, F. M.
, and Dearborn
, M. I.
, 1996, “FMEA Automation for the Complete Design Process
,” Proceedings 1996 Annual International Symposium on Product Quality and Integrity
, Las Vegas, NV, Jan. 22–25.29.
Perotti
, J.
, 2005, “Smart Sensors’ Role in Integrated System Health Management
,” Sensors for Industry Conference (SICon)
, Houston, TX, Feb. 8–10.30.
Santi
, L. M.
, Sowers
, T.
, and Aguilar
, R.
, 2005, “Optimal Sensor Selection for Health Monitoring Systems
,” 41st AIAA Joint Propulsion Conference
, Tucson, AZ, Jul., Paper No. AIAA-2005–4485.31.
Raiffa
, H.
, 1968, Decision Analysis: Introductory Lectures on Choices Under Uncertainty
, Addison-Wesley
, Reading, MA
.32.
Lewis
, E. E.
, 1996, Introduction to Reliability Engineering
, Wiley
, New York
.33.
Hoballah
, I. Y.
, and Varshney
, P. K.
, 1989, “Distributed Bayesian Signal Detection
,” IEEE Trans. Inf. Theory
0018-9448, 35
(5
), pp. 995
–1000
.34.
Viswanathan
, R.
, and Varshney
, P. K.
, 1997, “Distributed Detection With Multiple Sensors: I. Fundamentals
,” Proc. IEEE
0018-9219, 85
(1
), pp. 54
–63
.35.
Lough
, K. G.
, Stone
, R. B.
, and Tumer
, I.
, 2006, “The Risk in Early Design (RED) Method: Likelihood and Consequence Formulations
,” Proceedings of the 2006 ASME Design Engineering Technical Conference
, Philadelphia, PA, Sept. 10–13.Copyright © 2009
by American Society of Mechanical Engineers
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