Distributed Damage Estimation for Prognostics based on Structural Model Decomposition
Model-based prognostics approaches capture system knowledge in the form of physics-based models of components that include how they fail. These methods consist of a damage estimation phase, in which the health state of a component is estimated, and a prediction phase, in which the health state is projected forward in time to determine end of life. However, the damage estimation problem is often multi-dimensional and computationally intensive. We propose a model decomposition approach adapted from the diagnosis community, called possible conflicts, in order to both improve the computational efficiency of damage estimation, and formulate a damage estimation approach that is inherently distributed. Local state estimates are combined into a global state estimate from which prediction is performed. Using a centrifugal pump as a case study, we perform a number of simulation- based experiments to demonstrate the approach.
How to Cite
model-based prognostics, particle filters, distributed prognostics, centrifugal pump
Arulampalam, M. S., Maskell, S., Gordon, N., & Clapp, T. (2002). A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking. IEEE Transactions on Signal Processing, 50(2), 174–188.
Biswas, G., & Mahadevan, S. (2007, March). A Hierarchical model-based approach to systems health management. In Proc. of the 2007 IEEE Aerospace Conference.
Bregon, A., Pulido, B., & Biswas, G. (2009, Sep). Efficient on-line parameter estimation in TRANSCEND for non- linear systems. In Proc. of the Annual Conference of the Prognostics and Health Management Society 2009. San Diego, USA.
Bregon, A., Pulido, B., Biswas, G., & Koutsoukos, X. (2009). Generating possible conflicts from bond graphs using temporal causal graphs. In Proceedings of the 23rd European Conference on Modelling and Simulation (p. 675-682). Madrid, Spain.
Daigle, M., & Goebel, K. (2010a, 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. (2010b, March). Model-based prognostics under limited sensing. In Proceedings of the 2010 IEEE Aerospace Conference.
Daigle, M., & Goebel, K. (2011, March). Multiple damage progression paths in model-based prognostics. In Proceedings of the 2011 IEEE Aerospace Conference.
Doucet, A., Godsill, S., & Andrieu, C. (2000). On sequential Monte Carlo sampling methods for Bayesian filtering. Statistics and Computing, 10, 197–208.
Hutchings, I. M. (1992). Tribology: friction and wear of engineering materials. CRC Press.
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).
Kallesøe, C. (2005). Fault detection and isolation in centrifugal pumps. Unpublished doctoral dissertation, Aalborg University.
Katayama, T. (2005). Subspace Methods for System Identification. Springer.
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.
Lyshevski, S. E. (1999). Electromechanical Systems, Electric Machines, and Applied Mechatronics. CRC.
Overschee, P., & Moor, B. D. (1996). Subspace Identification for Linear Systems. Boston, MA, USA: Kluwer Academic Publishers.
Pulido, B., & Alonso-Gonza ́lez, C. (2004, October). Possible Conflicts: a compilation technique for consistency- based diagnosis. IEEE Trans. on Systems, Man, and Cybernetics. Part B: Cybernetics, 34(5), 2192-2206.
Roychoudhury, I., Biswas, G., & Koutsoukos, X. (2009, De- cember). Factoring dynamic Bayesian networks based on structural observability. In Proc. of the 48th IEEE Conference on Decision and Control (p. 244-250).
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.
Staroswiecki, M., & Declerck, P. (1989, July). Analytical redundancy in nonlinear interconnected systems by means of structural analysis. In IFAC Symp. on Advanced Information Processing in Automatic Control.
Westwick, D., & Verhaegen, M. (1996). Identifying MIMO Wiener systems using subspace model identification methods. Signal Processing, 52(2), 235 - 258.
Williams, B., & Millar, B. (1998). Decompositional model- based learning and its analogy to diagnosis. In Proc. of the Fifteenth National Conference on Artificial Intelligence (p. 197-204).
The Prognostic and Health Management Society advocates open-access to scientific data and uses a Creative Commons license for publishing and distributing any papers. A Creative Commons license does not relinquish the author’s copyright; rather it allows them to share some of their rights with any member of the public under certain conditions whilst enjoying full legal protection. By submitting an article to the International Conference of the Prognostics and Health Management Society, the authors agree to be bound by the associated terms and conditions including the following:
As the author, you retain the copyright to your Work. By submitting your Work, you are granting anybody the right to copy, distribute and transmit your Work and to adapt your Work with proper attribution under the terms of the Creative Commons Attribution 3.0 United States license. You assign rights to the Prognostics and Health Management Society to publish and disseminate your Work through electronic and print media if it is accepted for publication. A license note citing the Creative Commons Attribution 3.0 United States License as shown below needs to be placed in the footnote on the first page of the article.
First Author et al. This is an open-access article distributed under the terms of the Creative Commons Attribution 3.0 United States License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.