Abstract
The accurate estimation of helicopter component loads is an important factor in life cycle management and life extension efforts. This chapter explores continued efforts to utilize a number of computational intelligence algorithms, statistical and machine learning techniques, such as artificial neural networks, evolutionary algorithms, fuzzy sets, residual variance analysis, and others, to estimate some of these helicopter dynamic loads. For load prediction using indirect computational methods to be practical and accepted, demonstrating slight over-prediction of these loads is preferable to ensure that the impact of the actual load cycles is captured by the prediction and to incorporate a factor of safety. Subsequent calculation of the component’s fatigue life can verify the slight over-prediction of the load signal. This chapter examines a number of techniques for encouraging slight over-prediction and favoring a conservative estimate for these loads. Estimates for the main rotor normal bending on the Australian S-70-A-9 Black Hawk helicopter during a left rolling pullout at 1.5 g manoeuvre were generated from an input set consisting of thirty standard flight state and control system parameters. The results of this work show that when using a combination of these techniques, a reduction in under-prediction and increase in over-prediction can be achieved. In addition to load signal estimation, the component’s fatigue life and load exceedances can be estimated from the predicted load signal. In helicopter life cycle management, these metrics are more useful performance measures (as opposed to mean squared error or correlation of the load signal), therefore this chapter describes the process followed to calculate these measures from the load signal using Rainflow counting, material specific fatigue data (S-N curves), and damage theory. An evaluation of the proposed techniques based on the fatigue life estimates and/or load exceedances is also made.
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References
Lombardo, D.: Helicopter structures—a review of loads, fatigue design techniques and usage monitoring. Technical Report ARL-TR-15, Defence Science and Technology Organisation, 1993
Valdés, J.J., Cheung, C., Wang, W.: Evolutionary computation methods for helicopter loads estimation. In: 2011 IEEE Congress on Evolutionary Computation (CEC), pp. 1589–1596. June 2011
Valdés, J.J.: Computational intelligence methods for helicopter loads estimation. In: The 2011 International Joint Conference on Neural Networks (IJCNN), pp. 1864–1871. July 2011
Cheung, C., Rocha, B., Valdés, J.J., Kotwicz-Herniczek, M., Stefani, A.: Expanded fatigue damage and load time signal estimation for dynamic helicopter components using computational intelligence techniques. In: Proceedings of the American Helicopter Society 70th Annual Forum, Montreal, Canada, May 2014
Cheung, C., Rocha, B., Valdés, J.J., Stefani, A., Li, M.: An approach to fatigue damage estimation of helicopter rotating components using computational intelligence techniques. In: Proceedings of American Helicopter Society 69th Annual Forum, Phoenix, Arizona, May 2013
Georgia Tech Research Institute: Joint USAF-ADF S-70A-9 flight test program, summary report. Technical Report A-6186, Georgia Tech Research Institute, 2001
Cheung, C., Valdés, J.J., Li, M.: Exploration of flight state and control system parameters for prediction of helicopter loads via gamma test and machine learning techniques. In: Abou-Nasr, M., Lessmann, S., Stahlbock, R., Weiss, G.M. (eds.) Real World Data Mining Applications. Annals of Information Systems, vol. 17, pp. 359–385. Springer, Switzerland (2015)
Jones, A., Evans, D., Margetts, S., Durrant, P.: The gamma test. In: Sarker, R., Abbass, A., Newton, C. (eds) Heuristic Optimization for Knowledge Discovery, pp. 142–168. Idea Group, Hershey, PA (2002)
Stefánsson, A., Konc̆ar, N., Jones, A.: A note on the gamma test. Neural Comput. Appl. 5, 131–133 (1997)
Evans, D., Jones, A.: A proof of the gamma test. Proc. Roy. Soc. Lond. A 458, 1–41 (2002)
Anderberg, M.: Cluster Analysis for Applications. Wiley, London (1973)
Valdés, J.J., Cheung, C., Li, M.: Towards conservative helicopter loads prediction using computational intelligence techniques. In: The 2012 International Joint Conference on Neural Networks (IJCNN), pp. 1–8. June 2012
Cheung, C., Valdés, J.J., Li, M.: Use of evolutionary computation techniques for exploration and prediction of helicopter loads. In: 2012 IEEE Congress on Evolutionary Computation (CEC), pp. 1–8. June 2012
Chen, Z., Yang, Y.: Assessing forecast accuracy measures. http://www.stat.iastate.edu/preprint/articles/2004-10.pdf (2004)
Storn, R., Price, K.: Differential evolution-a simple and efficient adaptive scheme for global optimization over continuous spaces. Technical Report, TR-09012, ICSI, 1995
Price, K.V., Storn, R.M., Lampinen, J.A.: Differential Evolution. A Practical Approach to Global Optimization. Natural Computing Series. Springer, Heidelberg (2005)
Kukkonen, S., Lampinen, J.: An empirical study of control parameters for generalized differential evolution. Technical Report 2005014, Kanpur Genetic Algorithms Laboratory (KanGAL), 2005
G\(\ddot{{\rm a}}\)mperle, R., Müller, S., Koumoutsakos, P.: A parameter study for differential evolution. In: Grmela A., Mastorakis, N.E. (eds.) Advances in Intelligent Systems, Fuzzy Systems, Evolutionary Computation, pp. 293–298. WSEAS Press (2002)
Pres, W., Flannery, B., Teukolsky, S., Vetterling, W.: Numeric Recipes in C. Cambridge University Press, New York (1992)
Moller, M.F.: A scaled conjugate gradient algorithm for fast supervised learning. Neural Netw. 6, 525–533 (1993)
King, C., Lombardo, D.: Black Hawk helicopter component fatigue lives: sensitivity to changes in usag. Technical Report DSTO-TR-0912, Defence Science and Technology Organisation, 1999
Broek, D.: The Practical Use of Fracture Mechanics. Kluwer, Dordrecht (1989)
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This work was supported by Defence Research and Development Canada. Access to the Black Hawk data was granted by the Australian Defence Science and Technology Organisation.
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Cheung, C., Valdés, J.J., Puthuparampil, J. (2016). Improving Load Signal and Fatigue Life Estimation for Helicopter Components Using Computational Intelligence Techniques. In: Abielmona, R., Falcon, R., Zincir-Heywood, N., Abbass, H. (eds) Recent Advances in Computational Intelligence in Defense and Security. Studies in Computational Intelligence, vol 621. Springer, Cham. https://doi.org/10.1007/978-3-319-26450-9_22
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DOI: https://doi.org/10.1007/978-3-319-26450-9_22
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