Use of artificial neural networks for helicopter load monitoring

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Proceedings titleProceedings of the 7th DSTO International Conference on Health & Usage Monitoring
ConferenceAIAC14 Fourteenth Australian International Aerospace Congressm - 7th DSTO International Conference on Health & Usage Monitoring (HUMS2011), February 28-March 3, 2011, Melbourne, Australia
Subjectusage monitoring, helicopters, artificial neural networks
AbstractThe operational loads experienced by rotary-wing aircraft are more complex than those of fixed-wing aircraft due to the dynamic rotating components operating at high frequencies. As a result of the large number of load cycles produced by the rotating components and the wide load spectrum experienced from a rotary-wing aircraft’s broad range of manoeuvres, the fatigue lives of many components can be affected by even small changes in loads. Ongoing practical load monitoring methods have the potential to improve the accuracy of calculated component retirement times. Direct loads monitoring, however, can be difficult and oftentimes impractical with high equipment costs and large data storage requirements. This paper explores the potential of utilizing multi-layer artificial neural networks (ANNs) to determine airframe loads at fixed locations from flight state and control system (FSCS) parameters obtained during a Black Hawk flight load survey.
Publication date
AffiliationNRC Institute for Aerospace Research; National Research Council Canada
Peer reviewedYes
NPARC number19739558
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Record identifier9cd23c4d-7cd1-4568-9407-7c5a74c6331a
Record created2012-03-29
Record modified2016-05-09
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