Use of COTS Functional Analysis Software as an IVHM Design Tool for Detection and Isolation of UAV Fuel System Faults
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Abstract
This paper presents a new approach to the development of health management solutions which can be applied to both new and legacy platforms during the conceptual design phase. The approach involves the qualitative functional modelling of a system in order to perform an Integrated Vehicle Health Management (IVHM) design – the placement of sensors and the diagnostic rules to be used in interrogating their output. The qualitative functional analysis was chosen as a route for early assessment of failures in complex systems. Functional models of system components are required for capturing the available system knowledge used during various stages of system and IVHM design. MADeTM (Maintenance Aware Design environment), a COTS software tool developed by PHM Technology, was used for the health management design. A model has been built incorporating the failure diagrams of five failure modes for five different components of a UAV fuel system. Thus an inherent health management solution for the system and the optimised sensor set solution have been defined. The automatically generated sensor set solution also contains a diagnostic rule set, which was validated on the fuel rig for different operation modes taking into account the predicted fault detection/isolation and ambiguity group coefficients. It was concluded that when using functional modelling, the IVHM design and the actual system design cannot be done in isolation. The functional approach requires permanent input from the system designer and reliability engineers in order to construct a functional model that will qualitatively represent the real system. In other words, the physical insight should not be isolated from the failure phenomena and the diagnostic analysis tools should be able to adequately capture the experience bases. This approach has been verified on a laboratory bench top test rig which can simulate a range of possible fuel system faults. The rig is fully instrumented in order to allow benchmarking of various sensing solution for fault detection/isolation that were identified using functional analysis.
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Diagnosis and fault isolation methods, Fault Insertion Test, PHM sensors and detection methodologies, IVHM Design
Combitech, (2012), www.combitech.se/sv/Tjanster/Informationsforsorjning/Pro dukter--verktyg/
Glover W., Cross J., Lucas A., Stecki C., & Stecki J., (2010). The use of prognostic health management for autonomous unmanned air systems, Proceeding of International Conference on Prognostics and Health Management, October 10-16, Portland, Oregon, USA.
Gould E., (2004). Modelling it both ways: Hybrid diagnostic modelling and its application to hierarchical system design, DSI International, AUTOTESTCON, pp. 576-582.
Hilberth, K., (1995). Dependency modelling as an input to MIL-STD TRD/TPS development. AUTOTESTCON, pp. 552-555.
Hutcheson, R.S., McAdams, D.A. & Tumer I.Y., (2012). Function-based behavioural modelling. Int. J. Computer Aided Engineering and Technologies, Vol. 4, No. 3, pp. 250-271.
Impact Technologies, (2012), http://www.impact- tek.com/Resources/SellSheetPDFs/ReasonPro.pdf
ITI, (2012). http://www.itisim.com/simulationx/
Keller, K., Baldwin, A., Ofsthun, S., Swearingen, K., Vian, J., Wilmering, T., & Williams, Z., (2007). Health Management Engineering Environment and Open Integration Platform. Proceedings of IEEE Aerospace Conference, vol., no., pp.1-16 , paper no. 1319.
Kirschman, C. F., Fadel, G. M., & Jara-Almonte, C. C., (1996). Classifying functions for mechanical design. Proceedings of ASME Design Engineering Technical Conference and Computers in Engineering Conference, Paper ID 96-DETC/DTM1504, Irvine, August 18-22.
Kurtoglu, T., Johnson, S. B., Barszcz, E., Johnson, J. R., & Robinson, P. I. (2008). Integrating system health management into the early design of aerospace systems using Functional Fault Analysis, in Proeedings. Inernational Conference on Prognostics Health Management, Denver, CO, pp. 1–11.
Kurtoglu, T., & Tumer, I.Y., (2008). A graph-based fault identification and propagation framework for design of complex systems. Journal of Mecahnical Design, vol. 130, no. 5, pp. 051401-1-051401-8.
Kurtoglu, T., Narasimhan, S., Poll, S., Garcia, D., Kuhn, L., de Kleer, J., van Gemund, A., and Feldman, A., (2009). First international diagnosis competition - DXC'09, In Proc. DX'09, pp. 383-396.
Malin, J. T., & Throop, D. R., (2007). Basic concepts and distinctions for an Aerospace ontology of functions, entities and problems. in Proc. IEEAC, pp 1-18.
Pahl, G. & Beitz, W., (1988). Engineering Design: A Systematic Approach, Springer-Verlag. Berlin
Rudov-Clark, Stecki C., & Stecki, J., (2009). The language of FMEA: on the effective use and reuse of FMEA data, Sixth DSTO International Conference on Health & Usage Monitoring.
Rudov-Clark, S. J., Ryan, A.J., Stecki, C.M., & Stecki, J., (2009). Automated design and optimization of sensor sets for Condition –Based Monitoring, Sixth DSTO International Conference on Health & Usage Monitoring.
QSI (2012), Testability engineering and maintenance system (TEAMS) Tool, http://www.teamqsi.com/prods.html
SAE J905, (2009). "Fuel filter test methods", SAE International recommended practice
Stone, R.B., & Wood, K.L., (2000). Development of a functional basis for design, Journal of Mechanical Design, pp. 359-370.
Tumer, I.Y., & Smidts, C.S., (2011). Integrated Design- Stage Failure Analysis of Software-Driven Hardware Systems, Computers, IEEE Transactions on , vol.60, no.8, pp.1072-1084.
Ullman, D., (1997). The Mechanical Design Process. 2nd ed., McGraw-Hill.
Ulrich, K. & Eppinger, S., (1995), Product Design and Development, McGraw-Hill.
Walker, M., & Kapadia, R., (2009). Integrated Design of On-line Health and Prognostics Management, Proceeding of International Conference on Prognostics and Health Management. San Diego, CA September 27 – October 1, USA.
Wilmering, T.J., (2008). A practical examination of model reuse in support of health management application integration, AUTOTESTCON, pp. 189-194.
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