Prognostics for Autonomous Electric-Propulsion Aircraft
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Published
Mar 30, 2021
Johann Schumann
Chetan Kulkarni
Michael Lowry
Anupa Bajwa
Christopher Teubert
Jason Watkins
Abstract
An autonomous unmanned aerial system (UAS) needs, during the flight, accurate information about the current failure state of the aircraft and its capabilities in order to safely perform its mission and properly react to contingencies. The flight battery of an electric-propulsion aircraft is its most relevant resource. Model-based prognostics algorithms are used to obtain good estimates of its current state of charge and remaining capacity. However, these algorithms can have a large computational footprint. We present Prognostics-as-a-Service, a hybrid approach combining on-board computation with server-based prognostics on the ground.
In this paper, we focus on the role, battery prognostics plays for the safe operation of a highly autonomous aircraft: prognostics for (1) continuous on-board safety monitoring, (2) for UAS operations, and (3) for contingency planning. We present the NASA Autonomous Operating System (AOS) and discuss how the autonomous components closely work together with on-board and server-based ground prognostics systems. We will illustrate the system with case studies on small NASA unmanned aircraft.
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Keywords
Electric-Propulsion Aircraft, Prognostics
References
Ashby, M. J., & Byer, R. J. (2002). An approach for conducting a cost benefit analysis of aircraft engine prognostics and health management functions. In Proceedings of the 2002 IEEE Aerospace Conference (Vol. 6).
Balaban, E., & Alonso, J. (2012). An Approach to Prognostic Decision Making in the Aerospace Domain. In Proceedings of the Annual Conference of the Prognostics and Health Management Society 2012.
Beckman, B. C., Haskin, M., Rolnik, M., Vule, Y. (2017). Maneuvering a package following in-flight release from an unmanned aerial vehicle (UAV). Google Patents. (US Patent 9,567,081)
Caccamo, M. (2017). Power-aware emulation environment for long-endurance solar UAVs. Retrieved from rtsl-edge.cs.illinois.edu/UAV/inc/CNS-1646383 Poster Nov-2017.pdf
Corbetta, M., Banerjee, P., Okolo, W. A., Gorospe, G. E., Luchinsky, D. G. (2019). Real-time UAV Trajectory Prediction for Safety Monitoring in Low-Altitude Airspace. In AIAA 2019-3514. Session: UAS Traffic Management IV. doi: 10.2514/6.2019-3514
Daigle, M., Goebel, K. (2013). Model-based prognostics with concurrent damage progression processes. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 43(4), 535-546.
Daigle, M., Kulkarni, C. (2013). Electrochemistry-based Battery Modeling for Prognostics. In Proceedings of the Annual Conference of the Prognostics and Health Management Society 2013 (p. 249-261).
Daigle, M., Kulkarni, C., Gorospe, G. (2014). Application of Model-based Prognostics to a Pneumatic Valves Testbed. In Proceedings of the 2014 IEEE Aerospace Conference.
Daigle, M., Saha, B., Goebel, K. (2012). A comparison of filter-based approaches for model-based prognostics. In Proceedings of the 2012 IEEE Aerospace Conference.
Daigle, M., Saxena, A., Goebel, K. (2012). An efficient deterministic approach to model-based prediction uncertainty estimation. In Proceedings of the Annual Conference of the Prognostics and Health Management Society 2012 (p. 326-335).
DiFelici, J., Wargo, C. (2016). UAS safety planning and contingency assessment and advisory research. In 2016 Integrated Communications Navigation and Surveillance (ICNS) (pp. 8E3-1 – 8E3-16).
Federal Aviation Administration. (2020). Unmanned Aircraft System (UAS) Traffic Management (UTM): Concept of Operations V2.0. Retrieved from https://www.faa.gov/uas/research_development/traffic_management/media/UTM_ConOps_v2.pdf
Franke, J. L., Hughes, A., Jameson, S. C. (2006). Holistic contingency management for autonomous unmanned systems. In Proceedings of the AUVSIs Unmanned Systems North America.
Gasparovi´c, M., & Gajski, D. (2016). Unmanned Aerial Photogrammetric Systems in the Service of Engineering Geodesy. In International Symposium on Engineering Geodesy-SIG .
Geist, J., Rozier, K. Y., Schumann, J. (2014). Runtime Observer Pairs and Bayesian Network Reasoners Onboard FPGAs: Flight-Certifiable System Health Management for Embedded Systems. In Proceedings Runtime Verification (RV14) (pp. 215–230). Springer.
Gómez-Candón, D., De Castro, A., López-Granados, F. (2014). Assessing the accuracy of mosaics from unmanned aerial vehicle (UAV) imagery for precision agriculture purposes in wheat. Precision Agriculture, 15(1), 44–56.
Gorospe, G. E., Daigle, M. J., Sankararaman, S., Kulkarni, C. S., Ng, E. (2017). GPU Accelerated Prognostics. In Proceedings of the Annual Conference of the Prognostics and Health Management Society 2017.
Hess, A. (2002). Prognostics, from the need to reality-from the fleet users and PHM system designer/developers perspectives. In Proceedings of the 2002 IEEE Aerospace Conference (Vol. 6, p. 6-6). doi: 10.1109/AERO.2002.1036118
Hess, A., Calvello, G., Frith, P. (2005). Challenges, issues, and lessons learned chasing the ”Big P”. Real predictive prognostics. Part 1. In Proceedings of the 2005 IEEE Aerospace Conference (p. 3610-3619). doi: 10.1109/AERO.2005.1559666
Hogge, E., Bole, B., Vazquez, S., Strom, T., Hill, B., Smalling, K., Quach, C. (2015). Verification of a Remaining Flying Time Prediction System for Small Electric Aircraft. In Proceedings of the Annual Conference of the Prognostics and Health Management Society 2015.
Johnson, S., Gormley, T., Kessler, S., Mott, C., Patterson-Hine, A., Reichard, K., & Philip Scandura, J. (2011). System Health Management with Aerospace Applications. Wiley & Sons.
Jones, R. W., & Despotou, G. (2019). Unmanned aerial systems and healthcare: Possibilities and challenges. In 14th IEEE Conference on Industrial Electronics and Applications (ICIEA) (pp. 189–194).
Kulkarni, C., Daigle, M., Gorospe, G., & Goebel, G. (2014). Validation of Model-Based Prognostics for Pneumatic Valves in a Demonstration Testbed. In Proceedings of the Annual Conference of the Prognostics and Health Management Society 2014.
Lisso, G. K. (2017). Delivery of packages by unmanned aerial vehicles. Google Patents. (US Patent 9,536,216)
Lowry, M., Bajwa, A. R., Pressburger, T., Sweet, A., Dalal, M., Fry, C., . . . Mahadevan, N. (2018). Design Considerations for a Variable Autonomy Exeuctive for UAS in the NAS. In AIAA Information Systems-AIAA Infotech @ Aerospace.
Lowry, M., Pressburger, T., Dahl, D., & Dalal, M. (2019). Towards Autonomous Piloting: Communicating with Air Traffic Control. In AIAA Scitech Forum.
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.
McComas, D. (2012). NASA/GSFC’s Flight Software Core Flight System. In Flight Software Workshop.
Millar, R. C. (2007). A Systems Engineering Approach to PHM for Military Aircraft Propulsion Systems. In Proceedings of the 2007 IEEE Aerospace Conference (p. 1-9). doi: 10.1109/AERO.2007.352840
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, 18(4), 295-304.
Orsagh, R., Brown, D., Romer, M., Dabnev, T., Hess, A. (2005). Prognostic health management for avionics system power supplies. In Proceedings of the 2005 IEEE Aerospace Conference (p. 3585 - 3591).
Polka, M., Ptak, S., & Kuziora, L. (2017, 12). The Use of UAV’s for Search and Rescue Operations. Procedia Engineering, 192, 748-752.
Reinbacher, T., Rozier, K. Y., & Schumann, J. (2014). Temporal-Logic Based Runtime Observer Pairs for System Health Management of Real-Time Systems. In Tools and Algorithms for the Construction and Analysis of Systems - 20th International Conference, TACAS (Vol. 8413, pp. 357–372). Springer.
Rozier, K. Y., & Schumann, J. (2017). R2U2: Tool Overview. In Proceedings of RV-CuBES 2017 (pp. 138–156).
Sankararaman, S., & Teubert, C. (2017). Prospective architectures for onboard vs cloud-based decision making for unmanned aerial systems. Proceedings of the Annual Conference of the Prognostics and Health Management Society 2017.
Schumann, J., Mahadevan, N., Lowry, M., Karsai, G. (2019). Model-Based On-Board Decision Making for Autonomous Aircraft. In Proceedings of the Annual Conference of the Prognostics and Health Management Society 2019.
Schumann, J., Mahadevan, N., Sweet, A., Bajwa, A. R., Lowry, M., Karsai, G. (2019). Model-based System Health Management and Contingency Planning for Autonomous UAS. In AIAA Scitech Forum.
Schumann, J., Roychoudhury, I., Kulkarni, C. (2015). Diagnostic Reasoning using Prognostic Information for Unmanned Aerial Systems. In Proceedings of the Annual Conference of the Prognostics and Health Management Society 2015.
Schumann, J., Rozier, K. Y., Reinbacher, T., Mengshoel, O. J., Mbaya, T., Ippolito, C. (2015). Towards Realtime, On-board, Hardware-supported Sensor and Software Health Management for Unmanned Aerial Systems. International Journal of Prognostics and Health Management.
Swagger. (2020). Open API Specification. Retrieved from https://swagger.io/docs/specification/about/
Sweet, A., Gorospe, G., Daigle, M., Celaya, J. R., Balaban, E., Roychoudhury, I., Narasimhan, S. (2014). Demonstration of Prognostics-Enabled Decision Making Algorithms on a Hardware Mobile Robot Test Platform. In Proceedings of the Annual Conference of the Prognostics and Health Management Society 2014.
Teubert, C., Daigle, M. (2013). I/P Transducer Application of Model-Based Wear Detection and Estimation using Steady State Conditions. In Proceedings of the Annual Conference of the Prognostics and Health Management Society 2013 (p. 134-140).
Teubert, C., Daigle, M. (2014). Current/Pressure Transducer Application of Model-Based Prognostics using Steady State Conditions. In Proceedings of the 2014 IEEE Aerospace Conference.
Teubert, C., Daigle, M. J., Sankararaman, S., Goebel, K., Watkins, J. (2017). A Generic Software Architecture for Prognostics (GSAP). International Journal of Prognostics and Health Management, 8(2).
Thiels, C. A., Aho, J. M., Zietlow, S. P., Jenkins, D. H. (2015). Use of Unmanned Aerial Vehicles for Medical Product Transport. Air Medical Journal, 34(2), 104 – 108.
Tseng, C., Chau, C., Elbassioni, K. M., Khonji, M. (2017). Flight Tour Planning with Recharging Optimization for Battery-operated Autonomous Drones. CoRR, abs/1703.10049.
Verma, V., Jonsson, A., Pasareanu, C., & Iatauro, M. (2006). Universal Executive and PLEXIL: Engine and Language for Robust Spacecraft Control and Operations. In Spacecraft Control and Operations, American Institute of Aeronautics and Astronautics Space 2006 Conference.
Watkins, J., Teubert, C., Ossenfort, J. (2019). Prognostics As-A-Service: A Scalable Cloud Architecture for Prognostics. Proceedings of the Annual Conference of the Prognostics and Health Management Society 2019.
Wzorek, M., & Doherty, P. (2006). Reconfigurable Path Planning for an Autonomous Unmanned Aerial Vehicle. In International Conference on Hybrid Information Technology (Vol. 2, pp. 242–249).
Balaban, E., & Alonso, J. (2012). An Approach to Prognostic Decision Making in the Aerospace Domain. In Proceedings of the Annual Conference of the Prognostics and Health Management Society 2012.
Beckman, B. C., Haskin, M., Rolnik, M., Vule, Y. (2017). Maneuvering a package following in-flight release from an unmanned aerial vehicle (UAV). Google Patents. (US Patent 9,567,081)
Caccamo, M. (2017). Power-aware emulation environment for long-endurance solar UAVs. Retrieved from rtsl-edge.cs.illinois.edu/UAV/inc/CNS-1646383 Poster Nov-2017.pdf
Corbetta, M., Banerjee, P., Okolo, W. A., Gorospe, G. E., Luchinsky, D. G. (2019). Real-time UAV Trajectory Prediction for Safety Monitoring in Low-Altitude Airspace. In AIAA 2019-3514. Session: UAS Traffic Management IV. doi: 10.2514/6.2019-3514
Daigle, M., Goebel, K. (2013). Model-based prognostics with concurrent damage progression processes. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 43(4), 535-546.
Daigle, M., Kulkarni, C. (2013). Electrochemistry-based Battery Modeling for Prognostics. In Proceedings of the Annual Conference of the Prognostics and Health Management Society 2013 (p. 249-261).
Daigle, M., Kulkarni, C., Gorospe, G. (2014). Application of Model-based Prognostics to a Pneumatic Valves Testbed. In Proceedings of the 2014 IEEE Aerospace Conference.
Daigle, M., Saha, B., Goebel, K. (2012). A comparison of filter-based approaches for model-based prognostics. In Proceedings of the 2012 IEEE Aerospace Conference.
Daigle, M., Saxena, A., Goebel, K. (2012). An efficient deterministic approach to model-based prediction uncertainty estimation. In Proceedings of the Annual Conference of the Prognostics and Health Management Society 2012 (p. 326-335).
DiFelici, J., Wargo, C. (2016). UAS safety planning and contingency assessment and advisory research. In 2016 Integrated Communications Navigation and Surveillance (ICNS) (pp. 8E3-1 – 8E3-16).
Federal Aviation Administration. (2020). Unmanned Aircraft System (UAS) Traffic Management (UTM): Concept of Operations V2.0. Retrieved from https://www.faa.gov/uas/research_development/traffic_management/media/UTM_ConOps_v2.pdf
Franke, J. L., Hughes, A., Jameson, S. C. (2006). Holistic contingency management for autonomous unmanned systems. In Proceedings of the AUVSIs Unmanned Systems North America.
Gasparovi´c, M., & Gajski, D. (2016). Unmanned Aerial Photogrammetric Systems in the Service of Engineering Geodesy. In International Symposium on Engineering Geodesy-SIG .
Geist, J., Rozier, K. Y., Schumann, J. (2014). Runtime Observer Pairs and Bayesian Network Reasoners Onboard FPGAs: Flight-Certifiable System Health Management for Embedded Systems. In Proceedings Runtime Verification (RV14) (pp. 215–230). Springer.
Gómez-Candón, D., De Castro, A., López-Granados, F. (2014). Assessing the accuracy of mosaics from unmanned aerial vehicle (UAV) imagery for precision agriculture purposes in wheat. Precision Agriculture, 15(1), 44–56.
Gorospe, G. E., Daigle, M. J., Sankararaman, S., Kulkarni, C. S., Ng, E. (2017). GPU Accelerated Prognostics. In Proceedings of the Annual Conference of the Prognostics and Health Management Society 2017.
Hess, A. (2002). Prognostics, from the need to reality-from the fleet users and PHM system designer/developers perspectives. In Proceedings of the 2002 IEEE Aerospace Conference (Vol. 6, p. 6-6). doi: 10.1109/AERO.2002.1036118
Hess, A., Calvello, G., Frith, P. (2005). Challenges, issues, and lessons learned chasing the ”Big P”. Real predictive prognostics. Part 1. In Proceedings of the 2005 IEEE Aerospace Conference (p. 3610-3619). doi: 10.1109/AERO.2005.1559666
Hogge, E., Bole, B., Vazquez, S., Strom, T., Hill, B., Smalling, K., Quach, C. (2015). Verification of a Remaining Flying Time Prediction System for Small Electric Aircraft. In Proceedings of the Annual Conference of the Prognostics and Health Management Society 2015.
Johnson, S., Gormley, T., Kessler, S., Mott, C., Patterson-Hine, A., Reichard, K., & Philip Scandura, J. (2011). System Health Management with Aerospace Applications. Wiley & Sons.
Jones, R. W., & Despotou, G. (2019). Unmanned aerial systems and healthcare: Possibilities and challenges. In 14th IEEE Conference on Industrial Electronics and Applications (ICIEA) (pp. 189–194).
Kulkarni, C., Daigle, M., Gorospe, G., & Goebel, G. (2014). Validation of Model-Based Prognostics for Pneumatic Valves in a Demonstration Testbed. In Proceedings of the Annual Conference of the Prognostics and Health Management Society 2014.
Lisso, G. K. (2017). Delivery of packages by unmanned aerial vehicles. Google Patents. (US Patent 9,536,216)
Lowry, M., Bajwa, A. R., Pressburger, T., Sweet, A., Dalal, M., Fry, C., . . . Mahadevan, N. (2018). Design Considerations for a Variable Autonomy Exeuctive for UAS in the NAS. In AIAA Information Systems-AIAA Infotech @ Aerospace.
Lowry, M., Pressburger, T., Dahl, D., & Dalal, M. (2019). Towards Autonomous Piloting: Communicating with Air Traffic Control. In AIAA Scitech Forum.
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.
McComas, D. (2012). NASA/GSFC’s Flight Software Core Flight System. In Flight Software Workshop.
Millar, R. C. (2007). A Systems Engineering Approach to PHM for Military Aircraft Propulsion Systems. In Proceedings of the 2007 IEEE Aerospace Conference (p. 1-9). doi: 10.1109/AERO.2007.352840
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, 18(4), 295-304.
Orsagh, R., Brown, D., Romer, M., Dabnev, T., Hess, A. (2005). Prognostic health management for avionics system power supplies. In Proceedings of the 2005 IEEE Aerospace Conference (p. 3585 - 3591).
Polka, M., Ptak, S., & Kuziora, L. (2017, 12). The Use of UAV’s for Search and Rescue Operations. Procedia Engineering, 192, 748-752.
Reinbacher, T., Rozier, K. Y., & Schumann, J. (2014). Temporal-Logic Based Runtime Observer Pairs for System Health Management of Real-Time Systems. In Tools and Algorithms for the Construction and Analysis of Systems - 20th International Conference, TACAS (Vol. 8413, pp. 357–372). Springer.
Rozier, K. Y., & Schumann, J. (2017). R2U2: Tool Overview. In Proceedings of RV-CuBES 2017 (pp. 138–156).
Sankararaman, S., & Teubert, C. (2017). Prospective architectures for onboard vs cloud-based decision making for unmanned aerial systems. Proceedings of the Annual Conference of the Prognostics and Health Management Society 2017.
Schumann, J., Mahadevan, N., Lowry, M., Karsai, G. (2019). Model-Based On-Board Decision Making for Autonomous Aircraft. In Proceedings of the Annual Conference of the Prognostics and Health Management Society 2019.
Schumann, J., Mahadevan, N., Sweet, A., Bajwa, A. R., Lowry, M., Karsai, G. (2019). Model-based System Health Management and Contingency Planning for Autonomous UAS. In AIAA Scitech Forum.
Schumann, J., Roychoudhury, I., Kulkarni, C. (2015). Diagnostic Reasoning using Prognostic Information for Unmanned Aerial Systems. In Proceedings of the Annual Conference of the Prognostics and Health Management Society 2015.
Schumann, J., Rozier, K. Y., Reinbacher, T., Mengshoel, O. J., Mbaya, T., Ippolito, C. (2015). Towards Realtime, On-board, Hardware-supported Sensor and Software Health Management for Unmanned Aerial Systems. International Journal of Prognostics and Health Management.
Swagger. (2020). Open API Specification. Retrieved from https://swagger.io/docs/specification/about/
Sweet, A., Gorospe, G., Daigle, M., Celaya, J. R., Balaban, E., Roychoudhury, I., Narasimhan, S. (2014). Demonstration of Prognostics-Enabled Decision Making Algorithms on a Hardware Mobile Robot Test Platform. In Proceedings of the Annual Conference of the Prognostics and Health Management Society 2014.
Teubert, C., Daigle, M. (2013). I/P Transducer Application of Model-Based Wear Detection and Estimation using Steady State Conditions. In Proceedings of the Annual Conference of the Prognostics and Health Management Society 2013 (p. 134-140).
Teubert, C., Daigle, M. (2014). Current/Pressure Transducer Application of Model-Based Prognostics using Steady State Conditions. In Proceedings of the 2014 IEEE Aerospace Conference.
Teubert, C., Daigle, M. J., Sankararaman, S., Goebel, K., Watkins, J. (2017). A Generic Software Architecture for Prognostics (GSAP). International Journal of Prognostics and Health Management, 8(2).
Thiels, C. A., Aho, J. M., Zietlow, S. P., Jenkins, D. H. (2015). Use of Unmanned Aerial Vehicles for Medical Product Transport. Air Medical Journal, 34(2), 104 – 108.
Tseng, C., Chau, C., Elbassioni, K. M., Khonji, M. (2017). Flight Tour Planning with Recharging Optimization for Battery-operated Autonomous Drones. CoRR, abs/1703.10049.
Verma, V., Jonsson, A., Pasareanu, C., & Iatauro, M. (2006). Universal Executive and PLEXIL: Engine and Language for Robust Spacecraft Control and Operations. In Spacecraft Control and Operations, American Institute of Aeronautics and Astronautics Space 2006 Conference.
Watkins, J., Teubert, C., Ossenfort, J. (2019). Prognostics As-A-Service: A Scalable Cloud Architecture for Prognostics. Proceedings of the Annual Conference of the Prognostics and Health Management Society 2019.
Wzorek, M., & Doherty, P. (2006). Reconfigurable Path Planning for an Autonomous Unmanned Aerial Vehicle. In International Conference on Hybrid Information Technology (Vol. 2, pp. 242–249).
Section
Technical Papers