Demonstration of Prognostics-Enabled Decision Making Algorithms on a Hardware Mobile Robot Test Platform
Prognostics-enabled Decision Making (PDM) is an emerging research area that aims to integrate prognostic health information and knowledge about the future operating conditions into the process of selecting subsequent actions for the system. Previous work developing and testing PDM algorithms has been done in simulation; this paper describes the effort leading to a successful demonstration of PDM algorithms on a hardware mobile robot platform. The hardware platform, based on the K11 planetary rover prototype, was modified to allow injection of selected fault modes related to the rover’s electrical power subsystem. The PDM algorithms were adapted to the hardware platform, including development of a software module framework, a new route planner, and modifications to increase the algorithms’ robustness to sensor noise and system timing issues. A set of test scenarios was chosen to demonstrate the algorithms’ capabilities. The modifications to run with a hardware platform, the test scenarios, and the test results are described in detail. The results show a successful use of PDM algorithms on a hardware test platform to optimize mission planning in the presence of electrical system faults.
How to Cite
diagnosis, prognosis, decision-making
Balaban, E., Narasimhan, S., Daigle, M., Celaya, J., Roychoudhury, I., Saha, B., . . . Goebel, K. (2011, Septem- ber). A mobile robot testbed for prognostics-enabled autonomous decision making. In Annual conference of the prognostics and health management society 2011. Montreal, Canada.
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. International Journal of Prognostics and Health Management, 4(1).
Balaban, E., Saxena, A., Bansal, P., Goebel, K. F., & Curran, S. (2009). Modeling, detection, and disambiguation of sensor faults for aerospace applications. Sensors Journal, IEEE, 9(12), 1907–1917.
Balaban, E., Saxena, A., Narasimhan, S., Roychoudhury, I., Goebel, K., & Koopmans, M. (2010, September). Air- borne electro-mechanical actuator test stand for development of prognostic health management systems. In Annual conference of the prognostics and health management society 2010. Portland, OR.
Daigle, M., Bregon, A., Biswas, G., Koutsoukos, X., & Pulido, B. (2012, August). Improving multiple fault diagnosability using possible conflicts. In Proceedings of the 8th ifac symposium on fault detection, supervision and safety of technical processes (p. 144-149).
Daigle, M., Bregon, A., & Roychoudhury, I. (2014). Dis- tributed prognostics based on structural model decomposition. IEEE Transactions on Reliability.
Daigle, M., Koutsoukos, X., & Biswas, G. (2009, July). A qualitative event-based approach to continuous systems diagnosis. IEEE Transactions 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., Roychoudhury, I., Biswas, G., Koutsoukos, X., Patterson-Hine, A., & Poll, S. (2010, September). A comprehensive diagnosis methodology for complex hybrid systems: A case study on spacecraft power distribution systems. IEEE Transactions of Systems, Man, and Cybernetics, Part A, 4(5), 917–931.
Daigle, M., Roychoudhury, I., & Bregon, A. (2013, Octo- ber). Qualitative event-based diagnosis with possible conflicts: Case study on the fourth international diagnostic competition. In Proceedings of the 24th international workshop on principles of diagnosis (p. 230- 235).
Daigle, M., Saxena, A., & Goebel, K. (2012). An Efficient Deterministic Approach to Model-based Prediction Uncertainty Estimation. In Annual conference of the prognostics and health management society.
Gordon, N. J., Salmond, D. J., & Smith, A. F. (1993). Novel Approach to Nonlinear/non-Gaussian Bayesian State Estimation. IEE Proceedings F (Radar and Signal Processing), 140(2), 107–113.
face, version 3.0. (2014). Mountain View, California:Google Corporation.
Julier, S. J., & Uhlmann, J. K. (2004, March). Unscented filtering and nonlinear estimation. Proceedings of the IEEE, 92(3), 401–422.
LabVIEW version 22.214.171.12429. (2012). Austin, Texas: National Instruments Corporation.
Mosterman, P. J., & Biswas, G. (1999). Diagnosis of continuous valued systems in transient operating regions.IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans, 29(6), 554-565.
Poll, S., de Kleer, J., Abreau, R., Daigle, M., Feldman, A., Garcia, D., . . . Sweet, A. (2011, October). Third international diagnostics competition – DXC’11. In Proc. of the 22nd international workshop on principles of diagnosis (pp. 267–278).
Poll, S., Patterson-Hine, A., Camisa, J., Nishikawa, D., Spirkovska, L., Garcia, D., . . . others (2007, May). Evaluation, selection, and application of model- based diagnosis tools and approaches. In Aiaa in- fotech@aerospace 2007 conference and exhibit.
Reveley, M. S., Kurtoglu, T., Leone, K. M., Briggs, J. L., & Withrow, C. A. (2010, December). Assessment of the state of the art of integrated vehicle health management technologies as applicable to damage conditions (TM No. 2010-216911). Cleveland, OH: NASA.
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.
Smith, M., Byington, C., Watson, M., Bharadwaj, S., Swerdon, G., Goebel, K., & Balaban, E. (2009). Experimental and analytical development of health management for electro-mechanical actuators. In Ieee aerospace conference (pp. 1–14).
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