System Component Degradation: Filter Clogging in a UAV Fuel

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Published Jul 14, 2017
Zakwan Skaf Omer F. Eker Ian K. Jennions

Abstract

The filtration of possible contaminant is an essential part of many engineering processes in industry. Clogging of the filtration medium is one of the primary failure modes in many application areas leading to reduced performance and efficiency. Imitation of real life clogging scenarios in laboratory conditions is not an easy task to perform, but is demonstrated here, with the profiles obtained being injected into a fuel system rig. This paper shows generic results from two benchmark rigs. One is a fuel system laboratory testbed representing an Unmanned Aerial Vehicle (UAV) fuel system and its associated electrical power supply, control system and sensing capabilities. It is specifically designed in order to replicate a number of component degradation faults with a high degree of accuracy and repeatability. The second is a purpose built filter clogging rig designed to give
quality results to aid the development of prognostic algorithms. This paper’s contribution is to show results from the filter clogging rig and derive a transfer function, the relationship between filter clogging pressures and the fuel
system valve openings, to enable the fuel system rig to operate as if the clogging filter were part of the system. The results show that the local pressure drop obtained from the fuel rig can be made to closely match the pressure drop levels from the filter clogging rig. This opens up examination of the effects of filter clogging on the full fuel rig system, providing data for future system prognostic work.

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References
Baraldi, P., Di Maio, F., Mangili, F. and Zio, E. (2013). A belief function theory method for prognostics in clogging filters, Chemical Engineering Transactions, vol. 33, pp. 847-852.
Baraldi, P., Mangili, F. and Zio, E. (2015), A prognostics approach to nuclear component degradation modelling based on Gaussian Process Regression, Progress in Nuclear Energy, vol. 78, no. 0, pp. 141-154.
Benedettini, O., Baines, T. S., Lightfoot, H. W. and Greenough, R. M. (2009), State-of-the-art in integrated vehicle health management, Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering, vol. 223, no. 2, pp. 157-170.
Endo, Y., Chen, D.-R., and Pui, D. Y. H. (1998), Effects of Particle Polydispersity and Shape Factor During Dust Cake Loading on Air Filters, Poweder Technol, 98(3):241-249
Eker, Omer F., Camci, Fathi, Jennions, Ian K., 2016, Physics-based Modelling of Filter Clogging Phenomena, Mechanical Systems and Signal Processing 75, pp 395-412.
Jennions, I. K. (2011), Integrated Vehicle Health Management: Perspectives on an Emerging Field, SAE International.
Jones, M. (2008), Engine Fuel Filter Contamination, QTR_03 ed., Boeing AeroMagazine.
NASA (Oct. 1992), Research and technology goals and objectives for Integrated Vehicle Health Management (IVHM), report NASA-CR-192656.
Niculita, Octavian, Skaf, Zakwan, Jennions, Ian K., 2014, The Application of Bayesian Change Point Detection in UAV Fuel Systems, 3rd International Conference on Through-Life Engineering Services, Procedia CIRP 22 pp115-121, 7 pages.
Pontikakis, G. N., Koltsakis, G. C. and Stamatelos, A. M. (2001), Dynamic filtration modeling in foam filters for diesel exhaust, Chemical Engineering Communications, vol. 188, pp. 21-46.
Roussel, N., Nguyen, T. L. H. and Coussot, P. (2007), General probabilistic approach to the filtration process, Physical Review Letters, vol. 98, no. 11.
Roychoudhury, I., Hafiychuk, V. and Goebel, K. (2013), Model-based diagnosis and prognosis of a water recycling system, IEEE Aerospace Conference, 2013, pp. 1-9.
Saarela, O., Hulsund, J. E., Taipale, A. and Hegle, M. (2014), Remaining Useful Life Estimation for Air Filters at a Nuclear Power Plant, 2nd International Conference of the Prognostics and Health Management Society.
Sparks, T. (2011), Solid-Liquid Filtration: A User's Guide to Minimizing Cost & Environmental Impact, Maximizing Quality & Productivity, First Edition ed, Elsevier Science & Technology Books.
Sutherland, K. (2010), Mechanical engineering: The role of filtration in the machinery manufacturing industry, Filtration and Separation, vol. 47, no. 3, pp. 24-27.
Wilfong, D., Dallas, A., Yang, C., Johnson, P., Viswanathan, K., Madsen, M., Tucker, B. and Hacker, J. (2010), Emerging challenges of fuel filtration, Filtration, vol. 10, no. 2, pp. 107-117.
Section
Regular Session Papers