全部 标题 作者
关键词 摘要

OALib Journal期刊
ISSN: 2333-9721
费用:99美元

查看量下载量

相关文章

更多...

Differential Evolution: An Inverse Approach for Crack Detection

DOI: 10.1155/2013/321931

Full-Text   Cite this paper   Add to My Lib

Abstract:

This paper presents a damage detection technique combining analytical and experimental investigations on a cantilever aluminium alloy beam with a transverse surface crack. Firstly, the first three natural frequencies were determined using analytical methods based on strain energy release rate. Secondly, an experimental method was adopted to validate the theoretical findings. The damage location and severity assessment is the third stage and is formulated as a constrained optimisation problem and solved using the proposed differential evolution (DE) algorithm based on the measured and calculated first three natural frequencies as inputs. Numerical simulation studies indicate that the proposed method is robust and can be used effectively in structural health monitoring (SHM) applications. 1. Introduction A crack is a potential source of catastrophic failure in structures. Extensive investigations by researchers have been done to develop structural integrity monitoring techniques. Vibration measurement and analysis being an effective and convenient way to detect cracks in structures is mostly being used for development of various such techniques. Several nondestructive techniques (NDT) are available for local damage detection [1] using experimental methods like radiography, the magnetic field method, the acoustic method, and so forth. However, for health monitoring of critical and complex structures, in such experimental methods which require prior knowledge of the damage vicinity, accurate predictions may not be suitable. This has led to the development of quantitative global damage detection methods which are based on modal analysis [2, 3]. Researchers [4] also argue that in view of prohibitive costs and efforts involved in predicting damage to a high level accuracy a better idea is to roughly locate damage in the structure and then use standard NDT methods for closer analysis of the damaged area. Recently a lot of work has been done using modal analysis to detect, locate, and predict crack severity to a greater accuracy level. Dimarogonas [2] in 1996 provided a comprehensive review of vibration based mode shape analysis followed by some recent reviews [4–7]. The damage detection problem can be defined as a nonlinear inverse problem [3]. In conventional model-based detection methods, the minimization of an objective function is defined in terms of the differences between the vibration data obtained by modal testing and those computed from the analytical model. These conventional optimization methods are gradient based and usually lead to a local minimum

References

[1]  P. Cawley and R. D. Adams, “Location of defects in structures from measurements of natural frequencies,” Journal of Strain Analysis for Engineering Design, vol. 14, no. 2, pp. 49–57, 1979.
[2]  A. D. Dimarogonas, “Vibration of cracked structures: a state of the art review,” Engineering Fracture Mechanics, vol. 55, no. 5, pp. 831–857, 1996.
[3]  S. W. Doebling, C. F. Farrar, M. B. Prime, and D. W. Shevits, “Damage identification and health monitoring of structural and mechanical systems from changes in their vibration characteristics: a literature review,” Los Alamos National Laboratory, Los Alamos, NM, USA, 1996.
[4]  C. Boller, “Next generation structural health monitoring and its integration into aircraft design,” International Journal of Systems Science, vol. 31, no. 11, pp. 1333–1349, 2000.
[5]  D. Maity and R. R. Tripathy, “Damage assessment of structures from changes in natural frequencies using genetic algorithm,” Structural Engineering and Mechanics, vol. 19, no. 1, pp. 21–42, 2005.
[6]  W. Fan and P. Qiao, “Vibration-based damage identification methods: a review and comparative study,” Structural Health Monitoring, vol. 10, no. 1, pp. 83–111, 2011.
[7]  D. N. Thatoi, H. C. Das, Parhi, and D. R. :, “Review of techniques for fault diagnosis in damaged structure and engineering system,” Advances in Mechanical Engineering, vol. 2012, article 32756, 11 pages, 2012.
[8]  A. Sengupta and A. Upadhyay, “Locating the critical failure surface in a slope stability analysis by genetic algorithm,” Applied Soft Computing Journal, vol. 9, no. 1, pp. 387–392, 2009.
[9]  R. Perera, A. Ruiz, and C. Manzano, “An evolutionary multiobjective framework for structural damage localization and quantification,” Engineering Structures, vol. 29, no. 10, pp. 2540–2550, 2007.
[10]  X. Fang, H. Luo, and J. Tang, “Structural damage detection using neural network with learning rate improvement,” Computers and Structures, vol. 83, no. 25-26, pp. 2150–2161, 2005.
[11]  K. M. Saridakis, A. C. Chasalevris, C. A. Papadopoulos, and A. J. Dentsoras, “Applying neural networks, genetic algorithms and fuzzy logic for the identification of cracks in shafts by using coupled response measurements,” Computers and Structures, vol. 86, no. 11-12, pp. 1318–1338, 2008.
[12]  Y. M. Kim, C. K. Kim, and G. H. Hong, “Fuzzy set based crack diagnosis system for reinforced concrete structures,” Computers and Structures, vol. 85, no. 23-24, pp. 1828–1844, 2007.
[13]  P. Beena and R. Ganguli, “Structural damage detection using fuzzy cognitive maps and Hebbian learning,” Applied Soft Computing, vol. 11, no. 1, pp. 1014–1020, 2011.
[14]  F. Kang, J. J. Li, and Q. Xu, “Damage detection based on improved particle swarm optimization using vibration data,” Applied Soft Computing, vol. 12, no. 8, pp. 2329–2335, 2012.
[15]  B. Samanta and C. Nataraj, “Use of particle swarm optimization for machinery fault detection,” Engineering Applications of Artificial Intelligence, vol. 22, no. 2, pp. 308–316, 2009.
[16]  M. Clerc, “The swarm and the queen: towards a deterministic and adaptive particle swarm optimization,” in Proceeding of the International Coference on Entertainment Computing, pp. 1951–1957, Washington, DC, USA, I999.
[17]  M. Marinaki, Y. Marinakis, and G. E. Stavroulakis, “Fuzzy control optimized by PSO for vibration suppression of beams,” Control Engineering Practice, vol. 18, no. 6, pp. 618–629, 2010.
[18]  O. Begambre and J. E. Laier, “A hybrid Particle Swarm Optimization—Simplex algorithm (PSOS) for structural damage identification,” Advances in Engineering Software, vol. 40, no. 9, pp. 883–891, 2009.
[19]  M. T. V. Baghmisheh, M. Peimani, M. H. Sadeghi, M. M. Ettefagh, and A. F. Tabrizi, “A hybrid particle swarm—Nelder—mead optimization method for crack detection in cantilever beams,” Applied Soft Computing, vol. 12, no. 8, pp. 2217–2226, 2012.
[20]  P. M. Pawar and R. Ganguli, “Genetic fuzzy system for damage detection in beams and helicopter rotor blades,” Computer Methods in Applied Mechanics and Engineering, vol. 192, no. 16–18, pp. 2031–2057, 2003.
[21]  M. R. A. Rama, K. Lakshmi, and D. Venkatachalam :, “Damage diagnostic technique for structural health monitoring using POD and self adaptive differential evolution algorithm,” Computers and Structures, vol. 106-107, pp. 228–244, 2012.
[22]  T. W. Liao, “Two hybrid differential evolution algorithms for engineering design optimization,” Applied Soft Computing, vol. 10, no. 4, pp. 1188–1199, 2010.
[23]  S. Moradi and V. Tavaf, “Crack detection in circular cylindrical shells using differential quadrature method,” International Journal of Pressure Vessels and Piping, 2013.
[24]  L. Vincenzi, G. De Roeck, and M. Savoia, “Comparison between coupled local minimizers method and differential evolution algorithm in dynamic damage detection problems,” Advances in Engineering Software, vol. 65, pp. 90–100, 2013.
[25]  H. Tada, P. C. Paris, and G. R. Irwin, The Stress Analysis of Cracks Hand Book, Hellertown, Pa, USA, 1973.
[26]  R. Storn and K. Price, “Differential evolution—a simple and efficient heuristic for global optimization over continuous spaces,” Journal of Global Optimization, vol. 11, no. 4, pp. 341–359, 1997.
[27]  The Mathworks, MATLAB. 7.0.1.24704.

Full-Text

Contact Us

service@oalib.com

QQ:3279437679

WhatsApp +8615387084133