Name
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. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1109/CEC.2011.5949805 | IEEE Congress on Evolutionary Computation |
Keywords | Field | DocType |
aerodynamics,bending,blades,evolutionary computation,helicopters,learning (artificial intelligence),mechanical engineering computing,neural nets,product life cycle management,rotors,set theory,statistical analysis,trees (mathematics),bending,clustering,evolutionary computation methods,forward level flight,helicopter load estimation,life cycle management,life extension efforts,machine learning techniques,main rotor blade,model trees,neural networks,residual variance,time-dependent flight state system | Residual,Mathematical optimization,Ensemble forecasting,Computer science,White box,Evolutionary computation,Rotor (electric),Artificial intelligence,Cluster analysis,Artificial neural network,Genetic algorithm,Machine learning | Conference |
Citations | PageRank | References |
0 | 0.34 | 2 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Julio J. Valdés | 1 | 166 | 36.04 |
| Catherine Cheung | 2 | 0 | 1.01 |
| Weichao Wang | 3 | 500 | 33.87 |