Integrated Diagnostics and Prognostics for the Electrical Power System of a Planetary Rover

##plugins.themes.bootstrap3.article.main##

##plugins.themes.bootstrap3.article.sidebar##

Published Sep 29, 2014
Matthew Daigle Indranil Roychoudhury Anibal Bregon

Abstract

For electric vehicles, technology for monitoring, diagnosis, and prognosis of the electrical power system (EPS) becomes essential for safe and efficient operation. To this end, we develop a general system-level integrated diagnosis and prognosis framework, which detects, isolates, and identifies EPS faults, and predicts when the EPS will fail to deliver sufficient power. The approach takes advantage of recent work in structural model decomposition in order to distribute the global diagnosis and prognosis problems into local subproblems that can be solved in parallel, thus enabling implementation on distributed computational platforms. The framework is applied to the EPS of a planetary rover testbed, and is demonstrated using data from field experiments.

How to Cite

Daigle, . M. ., Roychoudhury, . I. ., & Bregon, A. (2014). Integrated Diagnostics and Prognostics for the Electrical Power System of a Planetary Rover. Annual Conference of the PHM Society, 6(1). https://doi.org/10.36001/phmconf.2014.v6i1.2375
Abstract 163 | PDF Downloads 103

##plugins.themes.bootstrap3.article.details##

Keywords

PHM

References
Balaban, E., Narasimhan, S., Daigle, M., Roychoudhury, I., Sweet, A., Bond, C., & Gorospe, G. (2013). Development of a mobile robot test platform and methods for validation of prognostics-enabled decision making algorithms. Intl. Journal of Prognostics and Health Management, 4(1).

Blanke, M., Kinnaert, M., Lunze, J., & Staroswiecki, M. (2006). Diagnosis and fault-tolerant control. Springer.

Bregon, A., Biswas, G., & Pulido, B. (2012). A Decomposition Method for Nonlinear Parameter Estimation in TRANSCEND. IEEE Trans. Syst. Man. Cy. Part A, 42(3), 751-763.

Bregon, A., Daigle, M., & Roychoudhury, I. (2012, Septem- ber). An integrated framework for model-based dis- tributed diagnosis and prognosis. In Annual conference of the prognostics and health management society 2012 (p. 416-426).

Bregon, A., Daigle, M., Roychoudhury, I., Biswas, G., Kout- soukos, X., & Pulido, B. (2014, May). An event-based distributed diagnosis framework using structural model decomposition. Artificial Intelligence, 210, 1-35.

Daigle, M. (2008). A qualitative event-based approach to fault diagnosis of hybrid systems. Unpublished doctoral dissertation, Vanderbilt University.

Daigle, M., Bregon, A., & Roychoudhury, I. (2012, Septem- ber). A distributed approach to system-level prognostics. In Annual conference of the prognostics and health management society 2012 (p. 71-82).

Daigle, M., Bregon, A., & Roychoudhury, I. (2014, June). Distributed prognostics based on structural model de- composition. IEEE Trans. on Reliability, 63(2), 495- 510.

Daigle, M., Koutsoukos, X., & Biswas, G. (2007, April). Distributed diagnosis in formations of mobile robots. IEEE Trans. on Robotics, 23(2), 353–369.

Daigle, M., Koutsoukos, X., & Biswas, G. (2009, July). A qualitative event-based approach to continuous systems diagnosis. IEEE Trans. on Control Systems Technology, 17(4), 780–793.

Daigle, M., & Kulkarni, C. (2013, October). Electrochemistry-based battery modeling for prognostics. In Annual conference of the prognostics and health management society 2013 (p. 249-261).

Daigle, M., & Kulkarni, C. (2014). A battery health monitoring framework for planetary rovers. In Proceedings of the IEEE aerospace conference.

Daigle, M., Roychoudhury, I., & Bregon, A. (2013, Octo- ber). Qualitative event-based diagnosis with possible conflicts: Case study on the fourth intl. diagnostic competition. In Proc. of the 24th intl. workshop on principles of diagnosis (p. 230-235).

Daigle, M., & Sankararaman, S. (2013, October). Advanced methods for determining prediction uncertainty in model-based prognostics with application to planetary rovers. In Annual conference of the prognostics and health management society 2013 (p. 262-274).

Daigle, M., Saxena, A., & Goebel, K. (2012, Septem- ber). An efficient deterministic approach to model- based prediction uncertainty estimation. In Annual conference of the prognostics and health management society (p. 326-335).

Gertler, J. J. (1998). Fault detection and diagnosis in engineering systems. New York, NY: Marcel Dekker, Inc. Julier, S. J., & Uhlmann, J. K. (2004, March). Unscented filtering and nonlinear estimation. Proceedings of the IEEE, 92(3), 401–422.

Karthikeyan, D. K., Sikha, G., & White, R. E. (2008). Thermodynamic model development for lithium intercalation electrodes. Journal of Power Sources, 185(2), 1398–1407.

Luo, J., Pattipati, K. R., Qiao, L., & Chigusa, S. (2008, September). Model-based prognostic techniques applied to a suspension system. IEEE Trans. on Systems, Man and Cybernetics, Part A: Systems and Humans, 38(5), 1156 -1168.

Mosterman, P. J., & Biswas, G. (1999). Diagnosis of continuous valued systems in transient operating regions. IEEE Trans. on Systems, Man, and Cybernetics, Part A: Systems and Humans, 29(6), 554-565.

Orchard, M. E., & Vachtsevanos, G. (2009). A particle- filtering approach for on-line fault diagnosis and failure prognosis. Trans. of the Institute of Measurement and Control, 31(3/4), 221-246.

Patrick, R., Orchard, M. E., Zhang, B., Koelemay, M., Kacprzynski, G., Ferri, A., & G., V. (2007, Septem- ber). An integrated approach to helicopter planetary gear fault diagnosis and failure prognosis. In Proc. of the 42nd annual systems readiness technology conf. Baltimore, MD, USA.

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: Cybernetics, 34(5), 2192-2206.

Rahn, C. D., & Wang, C.-Y. (2013). Battery systems engineering. Wiley.
Roychoudhury, I., & Daigle, M. (2011, October). An integrated model-based diagnostic and prognostic framework. In Proc. of the 22nd intl. workshop on principles of diagnosis (p. 44-51).

Roychoudhury, I., Daigle, M., Bregon, A., & Pulido, B. (2013, March). A structural model decomposition framework for systems health management. In Proceedings of the 2013 IEEE aerospace conference.

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.

Sankararaman, S., Daigle, M., & Goebel, K. (2014, June). Uncertainty quantification in remaining useful life pre- diction using first-order reliability methods. IEEE Trans. on Reliability, 63(2), 603-619.

Sankararaman, S., Daigle, M., Saxena, A., & Goebel, K. (2013, March). Analytical algorithms to quantify the uncertainty in remaining useful life prediction. In Proc. of the 2013 IEEE aerospace conference.
Section
Technical Research Papers