| Download | - View accepted manuscript: Evolutionary computation methods for helicopter loads estimation (PDF, 739 KiB)
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| DOI | Resolve DOI: https://doi.org/10.1109/CEC.2011.5949805 |
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| Author | Search for: Valdés, 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 | 2011 IEEE Congress on Evolutionary Computation (CEC), June 5-8, 2011, New Orleans, LA, USA |
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| Subject | Usage monitoring, Evolutionary computation methods, Artificial neural networks , Computational modeling , Genetic algorithms , Helicopters , Input variables , Predictive models , Training |
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| Abstract | The accurate estimation of component loads in a helicopter is an important goal for life cycle management and life extension efforts. This paper explores the use of evolutionary computational methods to help estimate some of these helicopter dynamic loads. Thirty standard time-dependent 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. Evolutionary computation methods (single and multi-objective genetic algorithms) optimizing residual variance, gradient, and number of predictor variables were employed to find subsets of the input variables with modeling potential. Clustering was used for composing a statistically representative training set. Machine learning techniques were applied for prediction of the main rotor blade normal bending involving neural networks, model trees (black and white box techniques) and their ensemble models. The results from this work demonstrate that reasonably accurate models for predicting component loads can be obtained using smaller subsets of predictor variables found by evolutionary computation based approaches. |
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| Publication date | 2011-06-08 |
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| In | |
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| Language | English |
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| Peer reviewed | No |
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| NPARC number | 18150445 |
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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 | 01c32fe6-0056-4670-aafc-94022f56c83f |
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| Record created | 2011-06-24 |
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| Record modified | 2020-04-21 |
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