Experimental Validation of Model-Based Prognostics for Pneumatic Valves
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Published
Nov 16, 2020
Chetan S. Kulkarni
Matthew J. Daigle
George Gorospe
Kai Goebel
Abstract
Because valves control many critical operations, they are prime candidates for deployment of prognostic algorithms. But, similar to the situation with most other components, examples of failures experienced in the field are hard to come by. This lack of data impacts the ability to test and validate prognostic algorithms. A solution sometimes employed to overcome this shortcoming is to perform run-to-failure experiments in a lab. However, the mean time to failure of valves is typically very high (possibly lasting decades), preventing evaluation within a reasonable time frame. Therefore, a mechanism to observe development of fault signatures considerably faster is sought. Described here is a testbed that addresses these issues by allowing the physical injection of leakage faults (which are the most common fault mode) into pneumatic valves. What makes this testbed stand out is the ability to modulate the magnitude of the fault almost arbitrarily fast. With that, the performance of end-of-life estimation algorithms can be tested. Further, the testbed is mobile and can be connected to valves
in the field. This mobility helps to bring the overall process of prognostic algorithm development for this valve a step closer to validation. The paper illustrates the development of a model-based prognostic approach that uses data from the testbed for partial validation.
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Keywords
prognostics, Pneumatic Valves, Remaining useful Life, Degradation Modeling
References
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Daigle, M., & Sankararaman, S. (2013, October). Advanced methods for determining prediction uncertainty in model-based prognostics with application to plane-tary rovers. In Annual conference of the prognostics and health management society 2013 (p. 262-274).
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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., 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.
Daigle, M. (2015, October). Real-time prognostics of a rotary valve actuator. In Annual conference of the prognostics and health management society 2015 (p. 46-56).
Daigle, M., & Goebel, K. (2011, August). A model-based prognostics approach applied to pneumatic valves. International Journal of Prognostics and Health Management, 2(2).
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 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 plane-tary rovers. In Annual conference of the prognostics and health management society 2013 (p. 262-274).
Fontana, M. (1986). Corrosion engineering. McGraw-Hill.
Gomes, J. P. P., Ferreira, B. C., Cabral, D., Glav˜ao, R. K. H., & Yoneyama, T. (2010, October). Health monitoring of a pneumatic valve using a PIT based technique. In Proceedings of the annual conference of the prognostics and health management society 2010.
Kulkarni, C., Daigle, M., & Goebel, K. (2013, September). Implementation of prognostic methodologies to cryogenic propellant loading testbed. In IEEE AUTOTESTCON
2013.
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.
Lin, Y.-H., Li, Y., & Zio, E. (2014, September). Dynamic reliability models for multiple dependent competing degradation processes. In Proceedings of the 24th european safety and reliability conference.
Lorton, A., Fouladirad, M., & Grall, A. (2013). Computation of remaining useful life on a physic-based model and impact of a prognosis on the maintenance process. Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability, 227(4), 434–449.
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.
Niculita, O., Jennions, I. K., & Irving, P. (2013, March). Design for diagnostics and prognostics: A physicalfunctional approach. In 2013 ieee aerospace conference.
Orchard, M., & Vachtsevanos, G. (2009, June). A particle filtering approach for on-line fault diagnosis and failure prognosis. Transactions of the Institute of Measurement and Control, 31(3-4), 221-246.
Perry, R., & Green, D. (2007). Perry’s chemical engineers’ handbook. McGraw-Hill Professional.
Poll, S., Patterson-Hine, A., Camisa, J., Garcia, D., Hall, D., Lee, C., . . . Koutsoukos, X. (2007, May). Advanced diagnostics and prognostics testbed. In 18th international workshop on principles of diagnosis (pp. 178–185).
Poll, S., Patterson-Hine, A., Camisa, J., Nishikawa, D., Spirkovska, L., Garcia, D., . . . Lutz, R. (2007, May). Evaluation, selection, and application of modelbased diagnosis tools and approaches. In AIAA infotechaerospace 2007 conference and exhibit.
Ribeiro, A. S., Yoneyama, T., Souto, R. F., & Turcio, W. (2015, October). Variable selection and indices proposal for the determination of an aeronautic valve degradation.
Richer, E., & Hurmuzlu, Y. (1999). A high performance pneumatic force actuator system: Part i—nonlinear mathematical model. In J. dyn. sys., meas., control (Vol. 122(3), p. 416-425).
Sankararaman, S., Daigle, M., & Goebel, K. (2014, June). Uncertainty quantification in remaining useful life prediction using first-order reliability methods. IEEE Transactions on Reliability, 63(2), 603-619.
Saxena, A., Celaya, J., Saha, B., Saha, S., & Goebel, K. (2010). Metrics for offline evaluation of prognostic performance. International Journal of Prognostics and Health Management, 1(1).
Shevach, G., Blair, M., Hing, J., Venetsky, L., Martin, E., & Wheelock, J. (2014, September). Towards performance prognostics of a launch valve. In Annual conference of the prognostics and health management society.
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.
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 Proceedings of the annual conference of the prognostics and health management society 2013 (p. 134-140).
Section
Technical Papers