An Approach to Prognostic Decision Making in the Aerospace Domain



Published Sep 23, 2012
Edward Balaban Juan J. Alonso


The field of Prognostic Health Management (PHM) has been undergoing rapid growth in recent years, with development of increasingly sophisticated techniques for diagnosing faults in system components and estimating fault progression trajectories. Research efforts on how to utilize prognostic health information (e.g. for extending the remaining useful life of the system, increasing safety, or maximizing operational effectiveness) are mostly in their early stages, however. The process of using prognostic information to determine a system’s actions or its configuration is beginning to be referred to as Prognostic Decision Making (PDM). In this paper we propose a formulation of the PDM problem with the attributes of the aerospace domain in mind, outline some of the key requirements for PDM methods, and explore techniques that can be used as a foundation of PDM development. The problem of satisfying the performance goals set for specific objective functions is discussed next, followed by ideas for possible solutions. The ideas, termed Dynamic Constraint Redesign (DCR), have roots in the fields of Multidisciplinary Design Optimization and Game Theory. Prototype PDM and DCR algorithms are also described and results of their testing are presented.

How to Cite

Balaban, . E. ., & J. Alonso, J. . (2012). An Approach to Prognostic Decision Making in the Aerospace Domain. Annual Conference of the PHM Society, 4(1).
Abstract 256 | PDF Downloads 170



prognostics, decision making, PDM

Agte, J. S., Weck, O., Sobieszczanski-Sobieski, J., Arendsen, P., Morris, A., & Spieck, M. (2009, April). MDO: Assessment and Direction for Advancement - an Opinion of One International Group. Structural and Multidisciplinary Optimization, 40(1-6), 17–33. doi: 10.1007/s00158-009-0381-5
Allison, J., Kokkolaras, M., Zawislak, M., & Papalambros, P. (2005). On the use of analytical target cascading and collaborative optimization for complex system design. In 6th world congress on structural and multidisciplinary optimization (pp. 1–10). Rio de Janeiro, Brazil.
Balaban, E., Narasimhan, S., Daigle, M., Celaya, J., Roy- choudhury, I., & Saha, B. (2011). A Mobile Robot Testbed for Prognostics-Enabled Autonomous Decision Making. In Annual conference of the prognostics and health management society (pp. 1–16). Montreal, Canada.
Benedettini, O., Baines, T. S., Lightfoot, H. W., & Gree- nough, R. M. (2009, March). State-of-the-art in integrated vehicle health management. Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering, 223(2), 157–170. doi: 10.1243/09544100JAERO446
Bertsimas, D., & Tsitsiklis, J. (1993). Simulated Annealing. Statistical Science, 8(1), 10–15.
Blouin, V. Y., Lassiter, J. B., Wiecek, M. M., & Fadel, G. M. (2005). Augmented Lagrangian Coordination for Decomposed Design Problems. In 6th world congress on structural and multidisciplinary optimization (pp. 1– 10). Rio de Janeiro, Brazil.
Bogdanov, A., Chiu, S., Gokdere, L. U., & Vian, J. (2006). Stochastic Optimal Control of a Servo Motor with a Lifetime Constraint. In Proceedings of the 45th ieee conference on decision and control (pp. 4182–4187). doi: 10.1109/CDC.2006.377205
Bole, B. (2012). Using Markov Models of Fault Growth Physics and Environmental Stresses to Optimize Control Actions. In Aiaa infotech @ aerospace (pp. 1–7). Garden Grove, CA.
Bole, B., Tang, L., Goebel, K., & Vachtsevanos, G. (2011). Adaptive Load-Allocation for Prognosis-Based Risk Management. In Annual conference of the prognostics and health management society (pp. 1–10).
Boularias, A. (2010). Predictive Representations For Sequential Decision Making Under Uncertainty. Unpublished doctoral dissertation, Laval University.
Braun, R., Gage, P., Kroo, I., & Sobiesky, I. (1996). Implementation and Performance Issues in Collaborative Optimization (Tech. Rep.). NASA Langley Research Center.
Brown, D. W., Georgoulas, G., & Bole, B. (2009). Prognostics Enhanced Reconfigurable Control of Electro- Mechanical Actuators. In Annual conference of the prognostics and health management society (pp. 1–17).Denver, CO.
Brown, D. W., & Vachtsevanos, G. J. (2011). A Prognos-
tic Health Management Based Framework for Fault- Tolerant Control. In Annual conference of the prognostics and health management society. Montreal, Canada.
Brown, N. (2004). Evaluation of Multidisciplinary Optimization (MDO) Techniques Applied to a Reusable Launch Vehicle (Tech. Rep.). Atlanta, GA: Georgia Institute of Technology.
Bryce, D., & Cushing, W. (2007). Probabilistic planning is multi-objective (Tech. Rep. No. Figure 1). Artificial Intelligence Center, SRI International, Inc.
Celaya, J., Saxena, A., & Saha, S. (2011). Prognos- tics of Power MOSFETs under Thermal Stress Accelerated Aging using Data-Driven and Model-Based Methodologies. In Annual conference of the prognostics and health management society (pp. 1–10). Montreal, Canada.
Clarich, A., & Pediroda, V. (2004). A Competitive Game Approach for Multi-Objective Robust Design Optimization. In 1st intelligent systems technical conference. Chicago, IL.
Coello, C., Lamont, G., & Veldhuizen, D. V. (2007). Evolutionary algorithms for solving multi-objective problems (2nd ed.). Springer.
Cramer, E. J., Dennis, J. E. J., Frank, P. D., Shubin, G. R., & Lewis, R. M. (1993). Problem Formulation for Multi- disciplinary Optimization. In Aiaa symposium on multidisciplinary design optimization (Vol. 4). SIAM. doi: 10.1137/0804044
Daigle, M., & Goebel, K. (2010, March). Model- based prognostics under limited sensing. 2010 IEEE Aerospace Conference, 1–12. doi: 10.1109/AERO.2010.5446822
Daigle, M., & Roychoudhury, I. (2010). Qualitative Event- Based Diagnosis: Case Study on the Second International Diagnostic Competition. In (pp. 1–8).
Das, A., & Chakrabarti, B. (2005). Quantum Annealing and Related Optimization Methods (Lecture Notes in Physics). Springer.
Delgado, I., Dempsey, P., & Simon, D. (2012). A Survey of Current Rotorcraft Propulsion Health Monitoring Technologies (NASA/TM2012-217420) (Tech. Rep. No. January). Cleveland, OH: NASA Glenn Research Center.
Denney, E., & Pai, G. (2012). Perspectives on Software Safety Case Development for Unmanned Aircraft. In Ieee/ifip international conference on dependable systems and networks (dsn). Boston, MA.
Driessen, B., & Kwok, K. (1998). A multiobjective dynamic programming approach to constrained discrete- time optimal control. In Proceedings of the 1998 American control conference. acc (ieee cat. no.98ch36207)
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