Valves are used in many domains and often have system- critical functions. As such, it is important to monitor the health of valves and their actuators and predict remaining useful life. In this work, we develop a model-based prognostics approach for a rotary valve actuator. Due to limited observability of the component with multiple failure modes, a lumped damage approach is proposed for estimation and pre- diction of damage progression. In order to support the goal of real-time prognostics, an approach to prediction is developed that does not require online simulation to compute remaining life, rather, a function mapping the damage state to remain- ing useful life is found offline so that predictions can be made quickly online with a single function evaluation. Simulation results demonstrate the overall methodology, validating the lumped damage approach and demonstrating real-time prognostics.
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actuator, prognostics, Real-Time Prognostics, model-based prognosis, valve
Bole, B. M., Brown, D. W., Pei, H.-L., Goebel, K., Tang, L., & Vachtsevanos, G. (2010). Load allocation for risk management in over actuated systems experiencing incipient failure conditions. In 2010 Conference on Control and Fault-Tolerant Systems (pp. 382–386).
Camci, F. (2009). System maintenance scheduling with prognostics information using genetic algorithm. IEEE Transactions on Reliability, 58(3), 539–552.
Daigle, M., & Goebel, K. (2009, September). Model-based prognostics with fixed-lag particle filters. In Proceed- ings of the Annual Conference of the Prognostics and Health
Management Society 2009.
Daigle, M., & Goebel, K. (2011a, August). A model-based prognostics approach applied to pneumatic valves. International Journal of Prognostics and Health Management, 2(2).
Daigle, M., & Goebel, K. (2011b, March). Multiple damage progression paths in model-based prognostics. In Proceedings of the 2011 IEEE Aerospace Conference.
Daigle, M., & Goebel, K. (2013, May). Model-based prognostics with concurrent damage progression processes. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 43(4), 535-546.
Daigle, M., Kulkarni, C., & Gorospe, G. (2014, March). 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, March). A comparison of filter-based approaches for model-based prognostics. In 2012 IEEE Aerospace Conference.
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., Sankararaman, S., & Kulkarni, C. (2015, March). Stochastic prediction of remaining driving time and distance for a planetary rover. In IEEE Aerospace Conference.
Graham, J. H., Dixon, R., Hubbard, P., & Harrington, I. (2014). Managing loads on aircraft generators to prevent overheat in-flight (Tech. Rep. No. 2014-01-2195). SAE.
Hutchings, I. M. (1992). Tribology: friction and wear of engineering materials. CRC Press.
Julier, S. J., & Uhlmann, J. K. (2004, Mar). Unscented filtering and nonlinear estimation. Proceedings of the IEEE, 92(3), 401–422.
Kulkarni, C., Daigle, M., Gorospe, G., & Goebel, K. (2014, September). Validation of model-based prognostics for pneumatic valves in a demonstration testbed. In Annual Conference of the Prognostics and Health Management Society 2014 (p. 76-85).
Kulkarni, C., Daigle, M., Gorospe, G., & Goebel, K. (2015, January). Application of model based prognostics to pneumatic valves in a cryogenic propellant loading testbed. In AIAA SciTech Conference.
Liu, J., & West, M. (2001). Combined parameter and state estimation in simulation-based filtering. Sequential Monte Carlo Methods in Practice, 197–223.
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.
Marante, M. E., & Flo ́rez-Lo ́pez, J. (2003). Three- dimensional analysis of reinforced concrete frames based on lumped damage mechanics. International Journal of Solids and Structures, 40(19), 5109–5123.
Orchard, M., Tobar, F., & Vachtsevanos, G. (2009, December). Outer feedback correction loops in particle filtering-based prognostic algorithms: Statistical per- formance comparison. Studies in Informatics and Control(4), 295-304.
Orchard, M., & Vachtsevanos, G. (2009, June). A particle filtering approach for on-line fault diagnosis and failure prognosis. Transactions of the Institute of Measure- ment and Control(3-4), 221-246.
Roychoudhury, I., Daigle, M., Bregon, A., & Pulido, B. (2013, March). A structural model decomposition framework for systems health management. In 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., Saxena, A., & Goebel, K. (2013, March). Analytical algorithms to quantify the uncertainty in remaining useful life prediction. In 2013 IEEE Aerospace Conference.
Tao, T., Zhao, W., Zio, E., Li, Y.-F., & Sun, J. (2014). Condition-based component replacement of the pneumatic valve with the unscented particle filter. In Prognostics and System Health Management Conference (pp. 290–296).
Teubert, C., & Daigle, M. (2013, October). I/P transducer application of model-based wear detection and estimation using steady state conditions. In Annual Conference of the Prognostics and Health Management Society 2013 (p. 134-140).
Teubert, C., & Daigle, M. (2014, March). Current/pressure transducer application of model-based prognostics using steady state conditions. In 2014 IEEE Aerospace Conference.
Tian, Z., Jin, T., Wu, B., & Ding, F. (2011). Condition based maintenance optimization for wind power generation systems under continuous monitoring. Renewable Energy, 36(5), 1502–1509.
Zeitlin, N. P., Clements, G. R., Schaefer, S. J., Fawcett, M. K., & Brown, B. L. (2013, March). Ground and launch systems processing technologies to reduce overall mission life cycle cost. In 2013 IEEE Aerospace Conference.
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