Download | - View accepted manuscript: Use of artificial neural networks for helicopter load monitoring (PDF, 1.2 MiB)
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Author | Search for: Liu, Andrew1; Search for: Cheung, Catherine1; Search for: Martinez, Marcias1 |
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Affiliation | - National Research Council of Canada. NRC Institute for Aerospace Research
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Format | Text, Article |
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Conference | AIAC14 Fourteenth Australian International Aerospace Congressm - 7th DSTO International Conference on Health & Usage Monitoring (HUMS2011), February 28-March 3, 2011, Melbourne, Australia |
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Subject | usage monitoring, helicopters, artificial neural networks |
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Abstract | The 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. |
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Publication date | 2011-03-03 |
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In | |
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Language | English |
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Peer reviewed | Yes |
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NPARC number | 19739558 |
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Export citation | Export as RIS |
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Report a correction | Report a correction (opens in a new tab) |
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Record identifier | 9cd23c4d-7cd1-4568-9407-7c5a74c6331a |
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Record created | 2012-03-29 |
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Record modified | 2020-04-21 |
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