Extremum selection method of random variable for nonlinear dynamic reliability analysis of turbine blade deformation

Propulsion and Power Research

Volumn 1, Issue 1, 2012, Pages 58-63

  Fei, Chengwei ^a   Bai, Guangchen ^a  

^a BEIHANG UNIVERSITY   (China)

Author keywords

Dynamic; Extremum selection method (ESM); Nonlinear; Radial deformation; Random variable; Reliability analysis; Turbine blade

Indexed keywords

EID: 85018199180     PISSN: None     EISSN: 2212540X     Source Type: Journal    
DOI: 10.1016/j.jppr.2012.10.010     Document Type: Article

References (16)

1. [1] Wu, L.H., Fang, J.R., Impact analysis of random variables selection on engineering reliability index. Water Conservancy Science and Technology of Zhejiang 140:4 (2005), 21–22.
2. [2] Wang, X.M., Li, Y.L., The variable choice and processing in the product optimization design. Research and Discussion 6 (2007), 35–37.
3. [3] K. Walden, The use and proper selection of variable speed drives (VSD) for reduced environmental footprint, sustainability and increased plant reliability, in: IEEE-IAS/PCA (Ed.), Proceedings of the 53th Cement Industry Technical Conference, 22–26 May 2011, Louis, Missouri, USA, pp. 1–9.
4. [4] K.L. Tang, Studies of variables selection and transformation method, Ph.D. Dissertation, School of Seienee, Tongji University, Shanghai, 2007.
5. [5] Rech, G., Teräsvirta, T., Tschernig, R., A simple variable selection technique for nonlinear models. Communications in Statistics: Theory and Methods 30:6 (2001), 1227–1241.
6. [6] Similä, T., Tikka, J., Combined input variable selection and model complexity control for nonlinear regression. Pattern Recognition Letters 30:3 (2009), 231–236.
7. [7] S.S. Monira, Z.M. Faisal, H. Hirose, A neural network ensemble incorporated with dynamic variable selection for rainfall forecast, in: IEEE/IET Electronic Library (Ed.), Proceedings of the 12th ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing, 6–8 July 2011, Sydney, NSW, pp. 7–12.
8. [8] Josep, M.M.T., François, E.C., Rafael, M.H., On the selection of variables for qualitative modeling of dynamical systems. International Journal of General Systems 31:5 (2002), 435–467.
9. [9] C. Anagnostopoulos, N. Adams, Simulating dynamic covariance structures for testing the adaptive behavior of variable selection algorithms, in: UKSIM (Ed.), Proceedings of the Tenth International Conference on Computer Modeling and Simulation, 1–3 July 2008, Washington, UK, pp. 52–57.
10. [10] J. Yi, T.F. Li, Y.Y. Su, W.J. Hu, T. Gao, A variable selection method based on KPCA and FNN for nonlinear system modeling, in: IEEE/IET Electronic Library (Ed.), Proceedings of the International Conference on Business Management and Electronic Information, 13–15 May 2011, Guangzhou, China, pp. 832–835.
11. [11] Liu, F., Jiang, Y.H., He, Y., Variable selection in visible/near infrared spectra for linear and nonlinear calibrations: a case study to determine soluble solids content of beer. Analytica Chimica Acta 635:1 (2009), 45–52.
12. [12] Yang, Y.P., Xue, L.G., Wang, X.J., Variable selection in high-dimensional partially linear models. Journal of Beijing University of Technology 37:2 (2011), 291–295.
13. [13] Li, T.F., Yi, J., Su, Y.Y., Hu, W.J., Gao, T., Variable selection for nonlinear modeling based on false nearest neighbors in KCCA subspace. Chinese Journal of Scientific Instrument 33:1 (2012), 203–210.
14. [14] Bertina, H.M.Q., Dayawansa, W.P., Non-smooth simultaneous stabilization of nonlinear systems: an interpolation method. Systems and Control Letters 34:1–2 (1998), 43–53.
15. [15] Ren, Y., Bai, G.C., New neural network response surface methods for reliability analysis. Chinese Journal of Aeronautics 24:1 (2011), 65–72.
16. [16] Zhang, C.Y., Bai, G.C., Extremum response surface method of reliability analysis on two-link flexible robot manipulator. Journal of Central South University of Technology 19:1 (2012), 101–107.