Advertisement

Airframe Loads and Usage Monitoring of the CH-47D “Chinook” Helicopter of the Royal Netherlands Air Force

  • A. Oldersma
  • M. J. Bos

Abstract

Prompted by severe structural maintenance issues, the Royal Netherlands Air Force has tasked the National Aerospace Laboratory NLR to develop an airframe loads & usage monitoring programme for their CH-47D helicopter fleet. After an initial pilot phase during which the technical and operational possibilities were explored, a routine programme named “CHAMP” (CHinook Airframe Monitoring Programme) was started in 2007. In addition to a fleet wide installation of a Cockpit Voice & Flight Data Recorder for the collection of the relevant parameters from the digital avionics data bus, two airframes have been equipped with a state-of-the-art data acquisition system and nine strain gauges each, which are recorded at a high sample rate. All data processing is performed off-board; no on-board data reduction is done. This has led to a vast and ever-growing database that can be used to conduct analyses that go beyond those traditionally performed within a loads & usage monitoring programme. This paper gives an overview of CHAMP and the underlying structural integrity concept that has been dubbed the “stethoscope method”. This method centres around the development of Artificial Neural Networks that use the recorded data bus parameters to predict internal loads at the strain gauge locations. After the creation of such a “virtual strain gauge”, the actual strain gauge can be relocated to monitor other key structural locations. Successive relocation of strain gauges finally results in a usage monitoring system that, in the long run, will be invaluable for structural life cycle management. Attention is paid to the acquisition and the off-board storage of the large sets of collected data, and an explanation is given of the analysis tools and methods that have been developed, and the results that have been achieved so far.

Keywords

Fatigue Damage Flight Regime Virtual Strain Usage Monitoring American Helicopter Society 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Helicopters, B.: RAF HCMk2A M4454 Ground and Flight Test Report - Appendix F: Flight Strain Survey D352-10077-1, Philadelphia (1998)Google Scholar
  2. 2.
    Robeson, E.: MH-47E Structural Usage Monitoring System (SUMS) Fleet Demonstration Results. In: American Helicopter Society 56th Annual Forum, Virginia Beach, USA (2000)Google Scholar
  3. 3.
    Harris, W.D., Larchuk, T., Zanoni, E., Zion, L.: Application of probabilistic methodology in the development of retirement lives of critical dynamic components in rotorcraft. In: American Helicopter Society 55th Annual Forum, Montreal, Canada (1999)Google Scholar
  4. 4.
    Adams, D.O., Kershner, S.D., Thielges, J.: Economical and reliable methods of processing HUMS data for maintenance credits. In: American Helicopter Society 55th Annual Forum, Montreal, Canada (1999)Google Scholar
  5. 5.
    ten Have, A.A., de Witte, P.J.H.H.: Enhanced platform availability through new FLM concepts, NLR Technical Publication TP-2008-415, Amsterdam, The Netherlands (2008) Google Scholar
  6. 6.
    Dickson, B., Cronkhite, J.D., Summers, H.: Usage and structural life monitoring with HUMS. In: American Helicopter Society 52nd Annual Forum, Washington D.C., USA (1996)Google Scholar
  7. 7.
    Teal, R.S., Evernham, J.T., Larchuk, T.J., Miller, D.G., Marquith, D.E., White, F., Deibler, D.T.: Regime Recognition for MH-47E Structural Usage Monitoring. In: American Helicopter Society 53rd Annual Forum, Virginia Beach, USA (1997)Google Scholar
  8. 8.
    Lu, Y., Christ, R.A., Puckett, T.A., Teal, R.S., Thompson, B.: AH-64D Apache Longbow Structural Usage Monitoring System (ALB SUMS). In: American Helicopter Society 58th Annual Forum, Montreal, Canada (2002)Google Scholar
  9. 9.
    Haas, D.J., Milano, J., Flitter, L.: Prediction of Helicopter Component Loads using Neural Networks. Journal of the American Helicopter Society 40(1) (1995)Google Scholar
  10. 10.
    Haas, D.J., Flitter, L., Milano, J.: Helicopter Flight Data Feature Extraction/ Component Load Monitoring. Journal of Aircraft 33(1) (1996)Google Scholar
  11. 11.
    Cabell, R.H., Fuller, C.R.: Neural Network Modelling of Oscillatory Loads and Fatigue Damage Estimation of Helicopter Components. Journal of Sound and Vibration 209(2) (1998)Google Scholar
  12. 12.
    Allen, M.J., Dibley, R.P.: Modeling Aircraft Wing Loads from Flight Data Using Neural Networks, NASA report NASA/TM-2003-212032 (2003)Google Scholar
  13. 13.
    Arms, S.W., Townsend, C.P., Galbreath, J.H., Liebschutz, D., Phan, N., Jones, A., Baker, T.: Flight testing of a wireless sensing system for rotorcraft CBM, Combined AHS and AIAA specialists’ meeting on Airworthiness, Condition Based Maintenance and Health/Usage Monitoring Systems, Huntsville, AL, USA (2011)Google Scholar
  14. 14.
    Brown, W.P., Steinmann, H.H.: The CH-47 Cruise Guide Indicator. Presented at the 26th Annual National Forum of the American Helicopter Society, Washington D.C., USA (1970)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • A. Oldersma
    • 1
  • M. J. Bos
    • 1
  1. 1.National Aerospace Laboratory NLRThe Netherlands

Personalised recommendations