The maintenance strategies of different industrial sectors are changing and evolving continuously in search of cost reduction, optimization of the operational reliability, the availability, and the resources used. The aeronautical industry belongs to this group. This article presents the global framework of a health management system as a new concept in aircraft line maintenance. This framework allows the transformation of the traditional maintenance (preventive and corrective, time based) into a predictive maintenance based on prognostic techniques. The article focuses in the development of the module ‘Condition View’, which has an essential role within this new model since it determines the deterioration and remaining useful life of critical aircraft parts. The case of detection and prediction of the aircraft break wear using a Bayesian net model that covers part of the functionality of ‘Condition View’ integrated in the global framework is presented.
TATEM (Technologies and Techniques for New Maintenance Concepts). FP6 EU Integrated Project; EC contract no. AIP3-CT-2004-502909, 2004.
2.
Lehane, M., Dubé, F., Halasz, M., Orchard, R., Wylie, R., and Zaluski, M. Integrated Diagnostic System (IDS) for Aircraft Fleet Maintenance. In Proceedings of the 15th National Conference on Artificial intelligence (AAAI '98 Workshop: case-based reasoning integrations), 26–30 July 1998, pp. 91–95 (Madison, Wisconsin, USA).
3.
LittJ. S.SimonD. L.GargS.GuoT.MercerC.MillarR.BehbahaniA.BajwaA.JensenD. T.A survey of intelligent control and health management technologies for aircraft propulsion systems. J. Aero. Comput. Inform. Comm., 2005, 1, 543–563.
4.
VolponiA.BrothertonT.LuppoldR.SimonD. L.Development of an information fusion system for engine diagnostics and health management. JANNAF 27th Air breathing Propulsion Subcommittee Meeting, 2003 (Colorado Springs, Colorado, USA)1–5 December 2003.
5.
Aaseng, G. B. Blueprint for a integrated vehicle health management system. The 20th Conference Digital Avionics Systems, 14–18 October 2001, vol. 1, pp. 3C1/1-3C1/11 (Daytona Beach, Florida, USA). DOI: 10.1109/DASC.2001.963350.
6.
BenedettiniO.BainesT. S.LightfootH. W.GreenoughR. M.State-of-the-art in integrated vehicle health management. Proc. IMechE Part G: J. Aerospace Engineering, 2008, 223, 157–170.
7.
Fox, J. J., and Glass, B. J. Impact of integrated vehiclehealth management(IVHM) technologies onground operations for reusable launch vehicles (RLVs) and spacecraft. In IEEE Aerospace Conference Proceedings, 18–25 March 2000, Vol. 2, pp. 179–186 (Big Sky, Montana, USA). DOI: 10.1109/AERO.2000.878223.
8.
MullerA.SuhnerM.IungB.Formalisation of a new prognosis model for supporting proactive maintenance implementation on industrial system. Reliab. Eng. Syst. Saf., 2008, 93, 234–253.
9.
LeeJ.NiJ.DjurdjanovicD.QiuH.LiaoH.Intelligent prognostics tools and e-maintenance. Comput. Ind., 2006, 57(6), 476–489.
10.
Byington, C. S., Roemer, M. J., and Galie, T. Prognostic enhancements to diagnostic systems for improved condition-based maintenance [military aircraft]. In Proceedings of the IEEE Aerospace Conference, 9–16 March 2002, vol. 6, pp. 2815–2824 (Big Sky, Montana, USA). DOI: 10.1109/AERO.2002.1036120.
11.
GreitzerF. L.PawloskiR. A.Embedded prognostics health monitoring. In Proceedings ofthe 48th International Instrumentation SymposiumEmbedded Health Monitoring Workshop, 5–9 May 2002 (USA).
12.
Puttini, L. C., and Fitzgibbon, K. T. An overview of testing methodologies for the development of PHM solutions. In 2008 International Conference on Prognostics and health management, 6–9 October 2008 (Denver, Colorado, USA).
13.
FeldmanK.JazouliT.SandbornP.A methodology for determining the return on investment associated with prognostics and health management. IEEE Trans. Reliab., 2009, 58, 305–316.
14.
Vohnout, S., Kozak, M., Goodman, D., Harris, K., and Judkins, J. Electronic prognostics system implementation on power actuator components. In 2008 IEEE aerospace conference, 1–8 March 2008, pp. 1–11. Big Sky, Montana, USA. DOI: 10.1109/AERO.2008.4526607.
15.
MalleyM. E.A methodology for simulating the joint strike fighter's (JSF) prognostics and health management system. MS Thesis AFIT/GOR/ENS/01M-11, 2001 (Air Force Institute of Technology (AU), Wright-Patterson AFB OH) March.
16.
Camci, F., Valentine, G. S., and Navarra, K. Methodologies for integration PHM systems with maintenance data. In Proceedings of 2007 IEEE Aerospace Conference, 3–10 March 2007, pp. 1–9. DOI: 10.1109/AERO.2007.352917.
17.
VachtsevanosG.LewisF. L.RoemerM.HessA.WuB.Intelligent fault diagnosis and prognosis for engineering systems, 2006 (John Wiley & Sons, New Jersey).
18.
GilabertE.ArnaizA.Intelligent automation systems for predictive maintenance: a case study. Robot. Comput. Integrated Manuf., 2006, 22, 543–549.
GoodeK. B.RoylanceB.Predicting the time to failure of critical components- a software package strategy. Proceedings of the Condition monitoring and diagnostic engineering management, 6–8 July 1999, 99, 547–555.
27.
Fayyad, U. M., and Irani, K. B. Multi-interval discretization of continous valued attributes for classification learning. In 13th International Joint Conference on Artificial Intelligence, 28 August–3 September 1993, pp. 1022–1027.
28.
ArnaizA.FerreiroS.New decision support system based on operational risk assessment to improve aircraft operability. Proc. IMechE., Part O: J. Risk and Reliability, 2010, 224, 137–147.
