Airborne Electro-Mechanical Actuator Test Stand for Development of Prognostic Health Management Systems
With the advent of the next generation of aerospace systems equipped with fly-by-wire controls, electromechanical actuators (EMA) are quickly becoming components critical to safety of aerospace vehicles. Being relatively new to the field, however, EMA lack the knowledge base compared to what is accumulated for the more traditional actuator types, especially when it comes to fault detection and prognosis. Scarcity of health monitoring data from fielded systems and prohibitive costs of carrying out real flight tests create the need to build high-fidelity system models and design affordable yet realistic experimental setups. The objective of this work is to build an EMA test stand that, unlike current laboratory stands typically weighing in excess of one metric ton, is portable enough to be easily placed aboard a wide variety of aircraft. This stand, named the FLEA (for Flyable Electromechanical Actuator test stand), allows testing EMA fault detection and prognosis technologies in flight environment, thus substantially increasing their technology readiness level – all without the expense of dedicated flights, as the stand is designed to function as a non-intrusive secondary payload. No aircraft modifications are required and data can be collected during any available flight opportunity: pilot currency flights, ferry flights, or flights dedicated to other experiments. The stand is currently equipped with a prototype version of NASA Ames developed prognostic health management system with models aimed at detecting and tracking several fault types. At this point the team has completed test flights of the stand on US Air Force C-17 aircraft and US Army UH-60 helicopters and more experiments, both laboratory and airborne, are planned for the coming months.
How to Cite
diagnosis, Electromechanical actuator, prognosis, EMA
Balaban, E., Saxena, A, .Bansal, P., Goebel, K.F., Stoelting, P, Curran, S., (2009). A Diagnostic Approach for Electro-Mechanical Actuators in Aerospace Systems, IEEE Aerospace Conference 2009, Big Sky MT, pp. 1-13.
Balaban, E., Saxena, A., Goebel, K., Byington, C., Watson, M., Bharadwaj, S., Smith, M., Amin, S., (2009). Experimental Data Collection and Modeling for Nominal and Fault Conditions on Electro- Mechanical Actuators, Annual conference of the PHM Society, PHM09, San Diego
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, vol.9, no.12, pp.1907-1917
Gertler. J. J., (1998), Fault Detection and Diagnosis in Engineering Systems, New Y ork, NY : Marcel Dekker, Inc.
Jensen, S. C., Jenney, G. D., and Dawson, D., (2000),Flight Test Experience with an Electromechanical Actuator on the F-18 Systems Research Aircraft, IEEE Digital Avionics Systems Conference.
Karnopp, D. C., Margolis, D. L., and Rosenberg, R. C., (2000), Systems Dynamics: Modeling and Simulation of Mechatronic Systems, 3rd ed. NewYork, NY, USA: John Wiley & Sons, Inc.
Mardia, K. V., Marshall, R. J., (1984), Maximum Likelihood Estimation for Models of Residual Covariance in Spatial Regression, Biometrika, vol 71(1), pp. 135-146
Mosterman, P. J. and Biswas, G., (1999), Diagnosis of continuous valued systems in transient operating regions, IEEE Transactions on Systems, Man and Cybernetics, Part A, vol. 29, no. 6, pp. 554-565.
Rasmussen, C. E. and Williams, C. K. I., (2006), Gaussian Processes for Machine Learning, The MIT Press.
Sorsa, T. and Koivo, H., (1998), Application of artificial neural networks in process fault diagnosis, Automatica, 29(4), pp. 843–849.
Smith, M.J. Byington, C.S. Watson, M.J. Bharadwaj, S. Swerdon, G. Goebel, K. Balaban, E., (2009), Experimental and Analytical Development of Health Management for Electro-Mechanical Actuators, IEEE Aerospace Conference, Big Sky, MT.
Swerdon, G., Watson, M.J., Bhardwaj, S., Byington, C.S., Smith, M., Goebel, K., Balaban, E., (2009), A Systems Engineering Approach to Electro- Mechanical Actuator Diagnostic and Prognostic Development, MFPT 2009, Society for Machinery Failure Prevention Technology, Dayton, OH.
Williams, C. K. I. and Rasmussen, C. E., (1996), Gaussian Processes for Regression, Touretzky, D. S., Mozer, M. C., and Hasselmo, M. E. (eds.), Advances in Neural Information Processing Systems, vol. 8, pp. 514-520, The MIT Press, Cambridge, MA
The Prognostic and Health Management Society advocates open-access to scientific data and uses a Creative Commons license for publishing and distributing any papers. A Creative Commons license does not relinquish the author’s copyright; rather it allows them to share some of their rights with any member of the public under certain conditions whilst enjoying full legal protection. By submitting an article to the International Conference of the Prognostics and Health Management Society, the authors agree to be bound by the associated terms and conditions including the following:
As the author, you retain the copyright to your Work. By submitting your Work, you are granting anybody the right to copy, distribute and transmit your Work and to adapt your Work with proper attribution under the terms of the Creative Commons Attribution 3.0 United States license. You assign rights to the Prognostics and Health Management Society to publish and disseminate your Work through electronic and print media if it is accepted for publication. A license note citing the Creative Commons Attribution 3.0 United States License as shown below needs to be placed in the footnote on the first page of the article.
First Author et al. This is an open-access article distributed under the terms of the Creative Commons Attribution 3.0 United States License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.