Prognostics, which deals with predicting remaining useful life of components, subsystems, and systems, is a key technology for systems health management that leads to improved safety and reliability with reduced costs. The prognostics problem is often approached from a component-centric view. However, in most cases, it is not specifically component life- times that are important, but, rather, the lifetimes of the systems in which these components reside. The system-level prognostics problem can be quite difficult due to the increased scale and scope of the prognostics problem and the relative lack of scalability and efficiency of typical prognostics approaches. In order to address these issues, we develop a distributed solution to the system-level prognostics problem, based on the concept of structural model decomposition. The system model is decomposed into independent submodels. Independent local prognostics subproblems are then formed based on these local submodels, resulting in a scalable, efficient, and flexible distributed approach to the system-level prognostics problem. We provide a formulation of the system-level prognostics problem and demonstrate the approach on a four-wheeled rover simulation testbed. The results show that the system-level prognostics problem can be accurately and efficiently solved in a distributed fashion.
How to Cite
model-based prognostics, distributed prognostics, system-level prognostics
Balaban, E., Narasimhan, S., Daigle, M., Celaya, J., Roy- choudhury, I., Saha, B., et al. (2011, September). A mobile robot testbed for prognostics-enabled autonomous decision making. In Annual conference of the prognostics and health management society (p. 15- 30). Montreal, Canada.
Balaban, E., Saxena, A., Narasimhan, S., Roychoudhury, I., Goebel, K., & Koopmans, M. (2010, September). Airborne electro-mechanical actuator test stand for development of prognostic health management systems. In Annual conference of the prognostics and health management society.
Bolander, N., Qiu, H., Eklund, N., Hindle, E., & Rosenfeld, T. (2010, October). Physics-based remaining useful life prediction for aircraft engine bearing prognosis. In Proceedings of the annual conference of the prognostics and health management society 2010.
Bregon, A., Biswas, G., & Pulido, B. (2012, May). A decomposition method for nonlinear parameter estimation in TRANSCEND. IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans, 42(3), 751-763.
Bregon, A., Daigle, M., & Roychoudhury, I. (2012, July). An integrated model-based distributed diagnosis and prognosis framework. In Proceedings of the 23rd international workshop on principles of diagnosis.
Byington, C. S., Watson, M., Edwards, D., & Stoelting, P. (2004, March). A model-based approach to prognostics and health management for flight control actuators. In Proceedings of the 2004 ieee aerospace conference (Vol. 6, pp. 3551–3562).
Celaya, J. R., Kulkarni, C., Biswas, G., Saha, S., & Goebel, K. (2011, September). A model-based prognostics methodology for electrolytic capacitors based on electrical overstress accelerated aging. In Proceedings of the annual conference of the prognostics and health management society 2011.
Daigle, M., Bregon, A., & Roychoudhury, I. (2011, Septem- ber). Distributed damage estimation for prognostics based on structural model decomposition. In Proceedings of the annual conference of the prognostics and health management society 2011 (p. 198-208).
Daigle, M., Bregon, A., & Roychoudhury, I. (2012). Dis- tributed prognostics based on structural model decom- position. (Manuscript submitted for publication.)
Daigle, M., & Goebel, K. (2010, October). Improving computational efficiency of prediction in model-based prognostics using the unscented transform. In Proc. of the annual conference of the prognostics and health management society 2010.
Daigle, M., & Goebel, K. (2011a, August). A model-based prognostics approach applied to pneumatic valves. International Journal of Prognostics and Health Management, 2(2).
Daigle, M., & Goebel, K. (2011b, March). Multiple damage progression paths in model-based prognostics. In Proceedings of the 2011 ieee aerospace conference.
Daigle, M., Saha, B., & Goebel, K. (2012, March). A comparison of filter-based approaches for model-based prognostics. In Proceedings of the 2012 ieee aerospace conference.
Julier, S. J., & Uhlmann, J. K. (1997). A new extension of the Kalman filter to nonlinear systems. In Proceedings of the 11th international symposium on aerospace/defense sensing, simulation and controls (pp. 182–193).
Julier, S. J., & Uhlmann, J. K. (2004, March). Unscented filtering and nonlinear estimation. Proceedings of the IEEE, 92(3), 401–422.
Liu, J., & West, M. (2001). Combined parameter and state estimation in simulation-based filtering. Sequential
Monte Carlo Methods in Practice, 197–223.
Luo, J., Pattipati, K. R., Qiao, L., & Chigusa, S. (2008, September). Model-based prognostic techniques applied to a suspension system. IEEE Transactions on Systems, Man and Cybernetics, Part A: Systems and
Humans, 38(5), 1156 -1168.
Moya, N., Biswas, G., Alonso-Gonzalez, C. J., & Kout-
soukos, X. (2010, October). Structural observability: Application to decompose a system with possible conflicts. In Proceedings of the 21st international workshop on principles of diagnosis (p. 241-248).
Mutambara, A. G. (1998). Decentralized estimation and control for multisensor systems. Boca Raton: CRC Press.
Orchard, M., Tobar, F., & Vachtsevanos, G. (2009, December). Outer feedback correction loops in particle filtering-based prognostic algorithms: Statistical performance comparison. Studies in Informatics and Control(4), 295-304.
Orchard, M., & Vachtsevanos, G. (2009, June). A particle filtering approach for on-line fault diagnosis and failure prognosis. Transactions of the Institute of Measurement and Control(3-4), 221-246.
Pulido, B., & Alonso-Gonza ́lez, C. (2004). Possible conflicts: a compilation technique for consistency-based diagnosis. IEEE Trans. on Systems, Man, and Cybernetics, Part B, Special Issue on Diagnosis of Complex Systems, 34(5), 2192-2206.
Pulido, B., Zamarreno, J., Merino, A., & Bregon, A. (2012, July). Using structural decomposition methods to design gray-box models for fault diagnosis of complex industrial systems: a beet sugar factory case study.
Saha, B., & Goebel, K. (2009, September). Modeling Li-ion battery capacity depletion in a particle filtering framework. In Proceedings of the annual conference of the prognostics and health management society 2009.
Saha, B., Saha, S., & Goebel, K. (2009). A distributed prognostic health management architecture. In Proceedings of the 2009 conference of the society for machinery failure prevention technology.
Saxena, A., Celaya, J., Saha, B., Saha, S., & Goebel, K. (2010). Metrics for offline evaluation of prognostic performance. International Journal of Prognostics and Health Management, 1(1).
Sinha, A., Chen, H., Danu, D., Kirubarajan, T., & Farooq, M. (2008). Estimation and decision fusion: A survey. Neurocomputing, 71(13), 2650–2656.
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