| Download | - View accepted manuscript: Computational intelligence methods for helicopter loads estimation (PDF, 969 KiB)
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| DOI | Resolve DOI: https://doi.org/10.1109/IJCNN.2011.6033451 |
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| Author | Search for: Valdes, Julio J.1; Search for: Cheung, Catherine2; Search for: Wang, Weichao2 |
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| Affiliation | - National Research Council of Canada. NRC Institute for Information Technology
- National Research Council of Canada. NRC Institute for Aerospace Research
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| Format | Text, Article |
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| Conference | The 2011 International Joint Conference on Neural Networks (IJCNN), July 31 - August 5, 2011, San Jose, California, USA |
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| Subject | Usage monitoring, Computational intelligence methods, Data models , Helicopters , Input variables , Predictive models , Rotors , Solid modeling |
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| Abstract | Accurately determining component loads on a helicopter is an important goal in the helicopter structural integrity field. While measuring dynamic component loads directly is possible, these measurement methods are not reliable and are difficult to maintain. This paper explores the potential of using computational intelligence methods to estimate some of these helicopter dynamic loads. Thirty standard timedependent flight state and control system parameters were used to construct a set of 180 input variables to estimate the main rotor blade normal bending during forward level flight at full speed. Unsupervised nonlinear mapping was used to study the structure of the multidimensional time series from the predictor and target variables. Based on these criteria, black and white box modeling techniques (including ensemble models) for main rotor blade normal bending prediction were applied. They include neural networks, local linear regression and model trees, in combination with genetic algorithms based on residual variance (gamma test) for predictor variables selection. The results from this initial work demonstrate that accurate models for predicting component loads can be obtained using the entire set of predictor variables, as well as with smaller subsets found by computational intelligence based approaches. |
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| Publication date | 2011-08-03 |
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| In | |
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| Language | English |
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| Peer reviewed | No |
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| NPARC number | 18150447 |
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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 | e1722e05-3ddc-4f06-853d-4dbb8e30dde1 |
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| Record created | 2011-06-24 |
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| Record modified | 2020-04-21 |
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