Development of a Mobile Robot Test Platform and Methods for Validation of Prognostics-Enabled Decision Making Algorithms
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Published
Oct 23, 2020
Edward Balaban
Sriram Narasimhan
Matthew J. Daigle
Indranil Roychoudhury
Adam Sweet
Christopher Bond
José R. Celaya
George Gorospe
Abstract
As fault diagnosis and prognosis systems in aerospace applications become more capable, the ability to utilize information supplied by them becomes increasingly important. While certain types of vehicle health data can be effectively processed and acted upon by crew or support personnel, others, due to their complexity or time constraints, require either automated or semi-automated reasoning. 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. The newly developed PDM algorithms require suitable software and hardware platforms for testing under realistic fault scenarios. The paper describes the development of such a platform, based on the K11 planetary rover prototype. A variety of injectable fault modes are being investigated for electrical, mechanical, and power subsystems of the testbed, along with methods for data collection and processing. In addition to the hardware platform, a software simulator with matching capabilities has been developed. The simulator allows for prototyping and initial validation of the algorithms prior to their deployment on the K11. The simulator is also available to the PDM algorithms to assist with the reasoning process. A reference set of diagnostic, prognostic, and decision making algorithms is also described, followed by an overview of the current test scenarios and the results of their execution on the simulator.
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Keywords
prognostics, testbed, decision-making
References
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Balaban, E., Narasimhan, S., Daigle, M., Celaya, J., Roychoudhury, I., Saha, B., . . . Goebel, K. (2011, September). A Mobile Robot Testbed for Prognostics-Enabled Autonomous Decision Making. In Annual Conference of the Prognostics and Health Management Society 2011. Montreal, Canada.
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Balaban, E., Saxena, A., Narasimhan, S., Roychoudhury, I., & Goebel, K. (2011). Experimental Validation of a Prognostic Health Management System for Electro- Mechanical Actuators. In AIAA Infotech@Aerospace.
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 2010. Portland, OR.
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 2011. Montreal, Canada.
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 2009. Denver, CO.
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Cassandra, A. R., Kaelbling, L. P., & Littman, M. L. (1995). Acting Optimally in Partially Observable Stochastic Domains. In Proceedings of the National Conference on Artificial Intelligence.
Celaya, J., Kulkarni, C., Biswas, G., & K., G. (2012). Towards A Model-based Prognostics Methodology for Electrolytic Capacitors: A Case Study Based on Electrical Overstress Accelerated Aging. International Journal of the Prognostics and Health Management Society, 3(2-004).
Ceraolo, M. (2000, November). New Dynamical Models of Lead-Acid Batteries. IEEE Transactions on Power Systems, 15(4), 1184–1190.
Chen, M.,&Rincon-Mora, G. (2006, June). Accurate Electrical Battery Model Capable of Predicting Runtime and I-V Performance. IEEE Transactions on Energy Conversion, 21(2), 504 - 511.
Daigle, M., & Goebel, K. (2011, March). Multiple Damage Progression Paths in Model-Based Prognostics. In IEEE Aerospace Conference.
Daigle, M., & Roychoudhury, I. (2010, October). Qualitative Event-based Diagnosis: Case Study on the Second International Diagnostic Competition. In Proc. of the 21st International Workshop on Principles of Diagnosis (pp. 371–378).
Daigle, M., Saha, B., & Goebel, K. (2012, March). A Comparison Of Filter-Based Approaches For Model-Based Prognostics. In IEEE Aerospace Conference.
Daigle, M., Saxena, A., & Goebel, K. (2012, September). An Efficient Deterministic Approach to Model-based Prediction Uncertainty Estimation. In Annual Conference of the Prognostics and Health Management Society 2012 (p. 326-335).
Edwards, D., Orchard, M. E., Tang, L., Goebel, K., & Vachtsevanos, G. (2010). Impact of Input Uncertainty on Failure Prognostic Algorithms : Extending the Remaining Useful Life of Nonlinear Systems. In Annual Conference of the Prognostics and Health Management Society 2010. Portland, OR.
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Mandow, A., Martínez, J., Morales, J., Blanco, J., García- Cerezo, A., & Gonzalez, J. (2007). Experimental Kinematics for Wheeled Skid-Steer Mobile Robots.
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Orchard, M. E. (2007). A Particle Filtering-Based Framework for On-Line Fault Diagnosis and Failure Prognosis. PhD Thesis, Georgia Institute of Technology.
Poll, S., et al. (2007, May). Evaluation, Selection, and Application of Model-Based Diagnosis Tools and Approaches. In AIAA Infotech@Aerospace 2007 Conference and Exhibit.
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Saha, B., & Goebel, K. (2009). Modeling Li-Ion Battery Capacity Depletion in a Particle Filtering Framework. In Annual Conference of the Prognostics and Health Management Society 2009.
Saha, B., Quach, C. C., & Goebel, K. (2012, March). Optimizing Battery Life for Electric UAV’s Using a Bayesian Framework. In IEEE Aerospace Conference. Schwabacher, M. (2005). A Survey of Data-Driven Prognostics. In Proceedings of the AIAA Infotech@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).
Tang, L., Hettler, E., Zhang, B., & Decastro, J. (2011). A Testbed for Real-Time Autonomous Vehicle PHM and Contingency Management Applications. In Annual Conference of the Prognostics and Health Management Society 2011.
Tang, L., Kacprzynski, G. J., Goebel, K., Reimann, J., Orchard, M. E., Saxena, A., & Saha, B. (2007). Prognostics in the Control Loop. In Working Notes of 2007 Fall AAAI Symposium (pp. 128–135).
Wolpert, D. (2006). Information Theory–The Bridge Connecting Bounded Rational Game Theory and Statistical Physics. Complex Engineered Systems, 262–290.
Balaban, E., Narasimhan, S., Daigle, M., Celaya, J., Roychoudhury, I., Saha, B., . . . Goebel, K. (2011, September). 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., Saxena, A., Bansal, P., Goebel, K., & Curran, S. (2009). Modeling, Detection, and Disambiguation of Sensor Faults for Aerospace Applications. IEEE Sensors Journal, 9(12), 1907–1917.
Balaban, E., Saxena, A., Narasimhan, S., Roychoudhury, I., & Goebel, K. (2011). Experimental Validation of a Prognostic Health Management System for Electro- Mechanical Actuators. In AIAA Infotech@Aerospace.
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 2010. Portland, OR.
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 2011. Montreal, Canada.
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 2009. Denver, CO.
Brown, D. W., & Vachtsevanos, G. J. (2011). A Prognostic Health Management Based Framework for Fault- Tolerant Control. In Annual Conference of the Prognostics and Health Management Society 2011. Montreal, Canada.
Cassandra, A. R., Kaelbling, L. P., & Littman, M. L. (1995). Acting Optimally in Partially Observable Stochastic Domains. In Proceedings of the National Conference on Artificial Intelligence.
Celaya, J., Kulkarni, C., Biswas, G., & K., G. (2012). Towards A Model-based Prognostics Methodology for Electrolytic Capacitors: A Case Study Based on Electrical Overstress Accelerated Aging. International Journal of the Prognostics and Health Management Society, 3(2-004).
Ceraolo, M. (2000, November). New Dynamical Models of Lead-Acid Batteries. IEEE Transactions on Power Systems, 15(4), 1184–1190.
Chen, M.,&Rincon-Mora, G. (2006, June). Accurate Electrical Battery Model Capable of Predicting Runtime and I-V Performance. IEEE Transactions on Energy Conversion, 21(2), 504 - 511.
Daigle, M., & Goebel, K. (2011, March). Multiple Damage Progression Paths in Model-Based Prognostics. In IEEE Aerospace Conference.
Daigle, M., & Roychoudhury, I. (2010, October). Qualitative Event-based Diagnosis: Case Study on the Second International Diagnostic Competition. In Proc. of the 21st International Workshop on Principles of Diagnosis (pp. 371–378).
Daigle, M., Saha, B., & Goebel, K. (2012, March). A Comparison Of Filter-Based Approaches For Model-Based Prognostics. In IEEE Aerospace Conference.
Daigle, M., Saxena, A., & Goebel, K. (2012, September). An Efficient Deterministic Approach to Model-based Prediction Uncertainty Estimation. In Annual Conference of the Prognostics and Health Management Society 2012 (p. 326-335).
Edwards, D., Orchard, M. E., Tang, L., Goebel, K., & Vachtsevanos, G. (2010). Impact of Input Uncertainty on Failure Prognostic Algorithms : Extending the Remaining Useful Life of Nonlinear Systems. In Annual Conference of the Prognostics and Health Management Society 2010. Portland, OR.
Fudenberg, D., & Tirole, J. (1991). Game Theory. MIT Press.
Genta, G. (1997). Motor Vehicle Dynamics: Modeling and Simulation. World Scientific Publishing Company Inc.
Henning, M. (2004). A New Approach to Object-Oriented Middleware. IEEE Internet Computing, 8(1), 66–75.
Hogge, E., Quach, C., Vazquez, S., & Hill, B. (2011). A Data System for a Rapid Evaluation Class of Subscale Aerial Vehicle (TM No. 2011-217145). NASA.
Huggins, R. (2008). Advanced Batteries: Materials Science Aspects. Springer.
Lachat, D., Krebs, A., Thueer, T., & Siegwart, R. (2006). Antarctica Rover Design And Optimization For Limited Power Consumption. In 4th IFAC Symp. on Mechatronic Systems.
Mandow, A., Martínez, J., Morales, J., Blanco, J., García- Cerezo, A., & Gonzalez, J. (2007). Experimental Kinematics for Wheeled Skid-Steer Mobile Robots.
In IEEE/RSJ International Conference on Intelligent Robots and Systems (p. 1222-1227).
Mosterman, P. J., & Biswas, G. (1999). Diagnosis of Continuous Valued Systems in Transient Operating Regions. IEEE Trans. on Sys., Man and Cybernetics, Part A, 29(6), 554-565.
Narasimhan, S., & Browston, L. (2007). Hyde - A General Framework For Stochastic And Hybrid Modelbased Diagnosis. In 18th International Workshop on Principles of Diagnosis (p. 162-169).
Orchard, M. E. (2007). A Particle Filtering-Based Framework for On-Line Fault Diagnosis and Failure Prognosis. PhD Thesis, Georgia Institute of Technology.
Poll, S., et al. (2007, May). Evaluation, Selection, and Application of Model-Based Diagnosis Tools and Approaches. In AIAA Infotech@Aerospace 2007 Conference and Exhibit.
Rasmussen, C., & Williams, C. (2006). Gaussian Processes for Machine Learning (Vol. 1). MIT press Cambridge, MA.
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). NASA.
Saha, B., & Goebel, K. (2009). Modeling Li-Ion Battery Capacity Depletion in a Particle Filtering Framework. In Annual Conference of the Prognostics and Health Management Society 2009.
Saha, B., Quach, C. C., & Goebel, K. (2012, March). Optimizing Battery Life for Electric UAV’s Using a Bayesian Framework. In IEEE Aerospace Conference. Schwabacher, M. (2005). A Survey of Data-Driven Prognostics. In Proceedings of the AIAA Infotech@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).
Tang, L., Hettler, E., Zhang, B., & Decastro, J. (2011). A Testbed for Real-Time Autonomous Vehicle PHM and Contingency Management Applications. In Annual Conference of the Prognostics and Health Management Society 2011.
Tang, L., Kacprzynski, G. J., Goebel, K., Reimann, J., Orchard, M. E., Saxena, A., & Saha, B. (2007). Prognostics in the Control Loop. In Working Notes of 2007 Fall AAAI Symposium (pp. 128–135).
Wolpert, D. (2006). Information Theory–The Bridge Connecting Bounded Rational Game Theory and Statistical Physics. Complex Engineered Systems, 262–290.
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Technical Papers