Cost-Benefit Analysis and Specification of Component-level PHM Systems in Aircrafts



Alexander Ka ̈hlert Sebastian Giljohann Uwe Klingauf


Unplanned aircraft ground times caused by component failures create costs for the operator through delays and reduced aircraft availability. Unscheduled maintenance on the other hand also creates costs for Maintenance, Repair and Overhaul (MRO) companies. The use of PHM is considered to improve the planning of component-specific maintenance and thus reduces consequential costs of unscheduled events on both sides.This study assesses the component-specific costs and characteristics of today’s maintenance approach. A discrete event simulation represents all relevant aircraft maintenance processes and dependencies. For this purpose the Event-driven Process Chain (EPC) method and Matlab/SimEvents are used. The data input (process information, empirical data) is provided by a particular MRO company.Whereas recent approaches deal with stochastically processed data only, e.g. failure probabilities, the proposed method mainly uses deterministic data. Empirical data, representing particular dependencies, describes all relevant stages in the component lifecycle. This includes operation, line and component maintenance, troubleshooting, planning and logistics.By simulating different scenarios, various maintenance future states can be evaluated by analysing effects on costs. The obtained economical and technical constraints allow to specify component-level PHM design parameters, as minimum prognostic horizon or accuracy. Detailed process-specific information is provided as well, e.g. costs of non-productive MRO activities or no-fault-found (NFF) characteristics.

How to Cite

Ka ̈hlert A. ., Giljohann, S. ., & Klingauf, U. . (2014). Cost-Benefit Analysis and Specification of Component-level PHM Systems in Aircrafts. Annual Conference of the PHM Society, 6(1).
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Monte Carlo, PHM, Simulation, LRU, Cost Benefit Analysis, no failure found, NFF, EPC, SimEvents

Air Transport Association of America. (2012).ATA iS- pec 2200 - Information Standards for Aviation Maintenance.

Bender, A., Pincombe, A. H., & Sherman, G. D. (2009). Effects of decay uncertainty in the prediction of life-cycle costing for large scale military capability projects. In 18th World IMACS/MODSIM Congress, Cairns, Australia. (Vol. 1).

Beynon-Davies, P. (2004). Database systems (3rd ed.). Basingstoke: Macmillan.

Cook, A. J., Tanner, G., & Anderson, S. (2004). Evaluating the true cost to airlines of one minute of airborne or ground delay: final report. Eurocontrol.

Eurocontrol. (2010). Eurocontrol Performance Review Commission: An Assessment of Air Traffic Management in Europe during the Calendar Year 2009.

Eurocontrol. (2011). Planning for Delay: Influence of Flight Scheduling on Airline Punctuality. EUROCONTROL Trends in Air Traffic(Volume 7).

Eurocontrol. (2012). Eurocontrol Central Office for Delay Analysis: CODA Digest - Delays to Air Transport in Europe November 2011.

Feldman, K., Jazouli, T., & Sandborn, P. A. (2009). A methodology for determining the return on investment associated with prognostics and health management. IEEE Transactions on Reliability, 58(2), 305–316.

Fritzsche, R., & Lasch, R. (2012). An Integrated Logistics Model of Spare Parts Maintenance Planning within the Aviation Industry. Proceedings of world academy of science, engineering and technology, Volume 68.

Fromm, H. B. (2009). Bewertung innovativer Instandhal- tungsszenarien in den fru ̈hen Phasen des Innovation- sprozesses in der Luftfahrt. Aachen: Shaker.

Gray, M. A. (2007). Discrete event simulation: A review of SimEvents. Computing in Science & Engineering, 9(6), 62–66.

Ho ̈lzel, N. B., Schilling, T., Neuheuser, T., Gollnick, V., & Lufthansa Technik AG. (2012). System Analysis of Prognostics and Health Management Systems for Future Transport Aircraft. In 28th International Congress of the Aeronautical Sciences (ICAS), Brisbane, Australia.

Knotts, R. M. (1999). Civil aircraft maintenance and support Fault diagnosis from a business perspective. Journal of Quality in Maintenance Engineering, 5(4), 335–348.

Kohn, W. (2005). Statistik: Datenanalyse und Wahrschein- lichkeitsrechnung. Berlin: Springer.

Linser, A. (2005). Performance Measurement in der Flugzeu- ginstandhaltung (Unpublished doctoral dissertation). Hochschule fu ̈r Wirtschafts-, Rechts-und Sozialwis- senschaften, St. Gallen.

Rodrigues, R. S., Balestrassi, P. P., Paiva, A. P., Garcia-Diaz, A., & Pontes, F. J. (2012). Aircraft interior failure pattern recognition utilizing text mining and neural networks. Journal of Intelligent Information Systems, 38(3), 741–766.

Saxena, A., Celaya, J., Balaban, E., Goebel, K., Saha, B., Saha, S., & Schwabacher, M. (2008). Metrics for evaluating performance of prognostic techniques. In 2008 International Conference on Prognostics and Health Management (pp. 1–17). IEEE.

Sisk, L. (1993). Analysing the value of maintenance in terms of despatch reliability. Cost Effectiveness Maintenance Conference.

Sun, B., Shengkui Zeng, Kang, R., & Pecht, M. (2010). Benefits analysis of prognostics in systems. In 2010 Prognostics and System Health Management Conference (pp. 1–8). IEEE.
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