Vibration reliability analysis for aeroengine compressor blade based on support vector machine response surface method

Abstract

To ameliorate reliability analysis efficiency for aeroengine components, such as compressor blade, support vector machine response surface method (SRSM) is proposed. SRSM integrates the advantages of support vector machine (SVM) and traditional response surface method (RSM), and utilizes experimental samples to construct a suitable response surface function (RSF) to replace the complicated and abstract finite element model. Moreover, the randomness of material parameters, structural dimension and operating condition are considered during extracting data so that the response surface function is more agreeable to the practical model. The results indicate that based on the same experimental data, SRSM has come closer than RSM reliability to approximating Monte Carlo method (MCM); while SRSM (17.296 s) needs far less running time than MCM (10958 s) and RSM (9840 s). Therefore, under the same simulation conditions, SRSM has the largest analysis efficiency, and can be considered a feasible and valid method to analyze structural reliability.

Keywords

vibration / reliability analysis / compressor blade / support vector machine response surface method / natural frequency

References

[1] RaoA R, DuttaB K. Vibration analysis for detecting failure of compressor blade [J]. Engineering Failure Analysis, 2012, 25: 211-218

[2] WitekL. Experimental crack propagation and failure analysis of the first stage compressor blade subjected to vibration [J]. Engineering Failure Analysis, 2009, 16(7): 2163-2170

[3] PoursaeidiE, BabaeiA, ArhaniM R M, ArhaiM R M. Effects of natural frequencies on the failure of R1 compressor blades [J]. Engineering Failure Analysis, 2012, 25: 304-315

[4] ArmstrongE K, WilliamsD D. Some intake flow mal distribution effects on compressor rotor blade vibration [J]. Journal of Sound and Vibration, 1966, 3(3): 340-354

[5] FitzpatrickC K, BaldwinM A, RullkoetterP J, LazP J. Combined probabilistic and principal component analysis approach for multivariate sensitivity evaluation and application to implanted patellofemoral mechanics [J]. Journal of Biomechanics, 2011, 44(1): 13-21

[6] ZonaA, BarbatoM, DallàstaA, DeziL. Probabilistic analysis for design assessment of continuous steel-concrete composite girders [J]. Journal of Constructional Steel Research, 2010, 66(7): 897-905

[7] PietrzykK, HagentoftC E. Probabilistic analysis of air infiltration in low-rise buildings [J]. Building and Environment, 2008, 43(4): 537-549

[8] NakamuraT, FujiiK. Probabilistic transient thermal analysis of an atmospheric reentry vehicle structure [J]. Aerospace Science and Technology, 2006, 10(4): 346-354

[9] HuD-y, WangR-q, TaoZhi. Probabilistic design for turbine disk at high temperature [J]. Aircraft Engineering and Aerospace Technology, 2011, 83(4): 199-207

[10] KartalM E, BasağaH B, BayraktarA. Probabilistic nonlinear analysis of CFR dams by MCS using response surface method [J]. Applied Mathematical Modeling, 2011, 35(6): 2752-2770

[11] LüQ, LowB K. Probabilistic analysis of underground rock excavations using response surface method and SORM [J]. Computers and Geotechnics, 2011, 38(8): 1008-1021

[12] MollonG, DiasD, SoubraA H. Probabilistic analysis of pressurized tunnels against face stability using collocation-based stochastic response surface method [J]. Journal Geotechnical and Geoenvironmental Engineering, 2010, 137(4): 385-397

[13] KimS H, NaS W. Response surface method using vector projected sampling points [J]. Structural Safety, 1997, 19(1): 3-19

[14] GuanX L, MelchersR E. Effect of response surface parameter variation on structural reliability estimates [J]. Structural Safety, 2001, 23(4): 429-444

[15] TanX-h, BiW-h, HouX-l, WangWei. Reliability analysis using radial basis function networks and support vector machines [J]. Computers and Geotechnics, 2011, 38(2): 178-186

[16] GuoZ-w, BaiG-chen. Application of least squares support vector machine for regression to reliability analysis [J]. Chinese Journal of Aeronautics, 2009, 22(2): 160-166

[17] das Chagas MouraM, ZioE, LinsI D, DroguettE. Failure and reliability prediction by support vector machines regression of time series data [J]. Reliability Engineering & System Safety, 2011, 96(11): 1527-1534

[18] ZhaoH-bo. Slope reliability analysis using a support vector machine [J]. Computers and Geotechnics, 2008, 35(3): 459-467

[19] RichardB, CremonaC, AdelaideL. A response surface method based on support vector machines trained with an adaptive experimental design [J]. Structural Safety, 2012, 39: 14-21

[20] VapnikVStatistical learning theory [M], 1998, New York, John Wiley and Sons

[21] VapnikVThe nature of statistical learning theory [M], 2000, New York, Springer-Verlag

[22] DengN-y, TianY-jieSupport vector machine theory, algorithm and expansion [M], 2009, Beijing, Scientific Press

[23] SuC-q, ZhangY-m, ZhaoQ-chao. Vibration reliability sensitivity analysis of general system with correlation failure modes [J]. Journal of Mechanical Science and Technology, 2011, 25(12): 3123-3133

[24] LiC, HanXing. Analysis of reliability sensitivity for gear engagement based on response surface methods [J]. Journal of Aerospace Power, 2011, 26(3): 711-715

[25] MaC, LuZ-zhou. Structural system reliability and sensitivity analysis based on support vector machine regression [J]. Acta Mechanica Solida Sinica, 2007, 28(4): 15-19

[26] ErsoyH, MuğanA. Design sensitivity analysis of structures based upon the singular value decomposition [J]. Computer Methods in Applied Mechanics and Engineering, 2002, 191(32): 3459-3476