Analysis of hybrid CSA-DEA method for fault detection of cracked structures
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School of Mechanical Engineering, Kalinga Institute of Industrial Technology, KIIT University, Bhubaneswar, Odisha, India
Department of Mechanical Engineering, National Institute of Technology, Rourkela, Odisha, India
Submission date: 2018-03-20
Acceptance date: 2018-11-19
Publication date: 2019-04-15
Journal of Theoretical and Applied Mechanics 2019;57(2):369-382
Formation of damage in a structural element often causes failures which is not desirable at all by a maintenance team. Identification of location and severity of damage can aid in taking necessary steps to reduce catastrophic failures of structures. As a result, non-destructive methods of damage detection have gained popularity over the last few years. In this paper, a method of damage detection is proposed to identify the location and severity of damage by hybridising a clonal selection algorithm with a differential evolution algorithm. The inputs to the hybrid system are the relative values of the first three natural frequencies of the damaged structure, and the outputs are relative crack locations and relative crack depths. For training the hybrid system, the natural frequencies are found out using finite element analysis and experimental analysis for different crack locations and crack depths. The test results from the proposed hybrid method are compared with finite element analysis and experimental analysis for validation, and satisfactory outcomes have been observed.
Abdullah A., Deris S., Anwar S., 2011, Hybrid evolutionary clonal selection for parameter estimation of biological model, International Journal of Computer Applications in Engineering Sciences, 1, 3, 313-319.
Brest J., Mauˇcec M.S., 2011, Self-adaptive differential evolution algorithm using population size reduction and three strategies, Soft Computing, 15, 11, 2157-2174.
Caddemi S., Morassi A., 2013, Multi-cracked Euler-Bernoulli beams: mathematical modeling and exact solutions, International Journal of Solids and Structures, 50, 6, 944-956.
Campelo F., Guimar˜aes F.G., Igarashi H., Ram´ırez J.A., 2005, A clonal selection algorithm for optimization in electromagnetics, IEEE Transactions on Magnetics, 41, 5, 1736-1739.
De Castro L.N., von Zuben F.J., 2000, The clonal selection algorithm with engineering applications, Proceedings of GECCO, 36-39.
Garain U., Chakraborty M.P., Dasgupta D., 2006, Recognition of handwritten indic script using clonal selection algorithm, International Conference on Artificial Immune Systems, 256-266.
Gong T., 2012, High-precision immune computation for secure face recognition, International Journal of Security and Its Applications (IJSIA), 6, 2, 293-298.
Gong T., Li L., Guo C., Gong X., 2012, Novel clonal selection algorithm improving selection operator, International Journal of Multimedia and Ubiquitous Engineering, 7, 2, 323-328.
Guo Z.G., Sun Z., 2011, Multiple cracked beam modeling and damage detection using frequency response function, Structural Longevity, 5, 2, 97-106.
Ling C.-X., Zhang H.-Q., Lin H., 2009, The modified clonal selection algorithm applied to the remote sensing image information extracting, WRI Global Congress on Intelligent Systems, 2, 94-102.
Manoach E., Warminska A., Warminski J., 2016, Dynamics of beams under coupled thermomechanical loading, Applied Mechanics and Materials, 849, 57-64.
Manoach E., Warminski J., Kloda L., Teter A., 2017, Numerical and experimental studies on vibration based methods for detection of damage in composite beams, Composite Structures, 170, 26-39.
Pawar R.S., Sawant S.H., 2014, An overview of vibration analysis of cracked cantilever beam with non-linear parameters and harmonic excitations, International Journal of Innovative Technology and Exploring Engineering, 8, 3, 53-55.
Pölöskei T., Szekrényes, A., 2017, Quasi-periodic excitation in a delaminated composite beam, Composite Structures, 159, 677-688.
Pölöskei T., Szekrényes, A., 2018, Dynamic stability of a structurally damped delaminated beam using higher order theory, Mathematical Problems in Engineering, DOI: 10.1155/2018/2674813.
Qin A.K., Suganthan P.N., 2005, Self-adaptive differential evolution algorithm for numerical optimization, The 2005 IEEE Congress on Evolutionary Computation, 2, 1785-1791.
Rahimi S., Soltani M., 2015, A stochastic simulation algorithmfor evaluation of seismic pounding response of adjacent structures, Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability, 229, 6, 517-529.
Sinha J.K., Friswell M.I., Edwards S., 2002, Simplified models for the location of cracks in beam structures using measured vibration data, Journal of Sound and Vibration, 251, 1, 13-38.
Tada H., Paris P.C., Irwin G.R., 1973, The Stress Analysis of Cracks. Handbook, Del Research Corporation.
Tang Y., 2012, Parameter estimation of Wiener model using differential evolution algorithm, International Journal of Circuits, Systems and Signal Processing, 6, 5, 315-323.
Timmis J., Hone A., Stibor T., Clark E., 2008, Theoretical advances in artificial immune systems, Theoretical Computer Science, 403, 1, 11-32.
Xie J., Hu Y., Zhu H., Wang Y., 2013, Surname inherited algorithm research based on artificial immune system, Indonesian Journal of Electrical Engineering and Computer Science, 11, 6, 3194-3199.
Xu J., Sun K., Xu L., 2015, Data mining-based intelligent fault diagnostics for integrated system health management to avionics, Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability, 229, 1, 3-15.
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