An approach for damage detection of space structures using combination of second order gradient and fuzzy logic methods

[1] D. K. Agarwalla, S. K. Abdul, S. K. Sahoo, Application of genetic fuzzy system for damage identification in cantilever beam structure, Procedia Engineering, 144 (2016), 215-225.

[2] J. S. Arora, Introduction to optimum design, McGraw-Hill New York, 1989.

[3] P. Beena, G. Ranjan, Structural damage detection using fuzzy cognitive maps and Hebbian learning, Applied Soft Computing Journal, 11(1) (2010), 1014-1020.

[4] M. Chandrashekhar, G. Ranjan, Damage assessment of structures with uncertainty by using mode-shape curvatures and fuzzy logic, Journal of Sound and Vibration, 326(3-5) (2009), 939-957.

[5] M. Chandrashekhar, G. Ranjan, Uncertainty handling in structural damage detection using fuzzy logic and probabilistic simulation, Mechanical Systems and Signal Processing, 23 (2009), 384-404.

[6] D. Dinh-Cong, V. Ho-Huu, T. Vo-Duy, H. Q. Ngo-Thi, T. Nguyen-Thoi, Efficiency of Jaya algorithm for solving the optimization-based structural damage identification problem based on a hybrid objective function, Engineering Optimization, 50(8) (2018), 1233-1251.

[7] S. S. Edward, K. Powsiri, V. S. G. Hota, B. H. Udaya, Fuzzy logic expert system for automated damage detection from changes in strain energy mode shapes, Nondestructive Testing and Evaluation, 18(1) (2002), 1-20.

[8] M. M. Ettefagh, M. H. Sadeghi, S. Khanmohammadi, Structural damage detection using fuzzy classification and ARMA parametric modeling, Aerospace Mechanics Journal, 3(2) (2007), 85-98.

[9] S. J. S. Hakima, R. H. Abdul, Adaptive neuro fuzzy inference system (ANFIS) and artificial neural networks (ANNs) for structural damage identification, Structural Engineering and Mechanics, 45(6) (2013), 779-802.

[10] S. S. Hameed, V. Muralidharan, B. K. Ane, Comparative analysis of fuzzy classifier and ANN with histogram features for defect detection and classification in planetary gearbox, Applied Soft Computing, 106 (2021). DOI:10.1016/j.asoc.2021.107306.

[11] S. S. Hameed, V. Muralidharan, D. P. Kumar, S. Ravikumar, Fault classification using fuzzy logic in an Epicyclic Gearbox with statistical features, SAE Technical Paper, (2021), 7 pages, DOI:10.4271/2021-28-0220.

[12] R. J. Hansen, A numerical method for solving Fredholm integral equations of the first kind using singular values, SIAM Journalon Numerical Analysis, 8(3) (1971), 616-622.

[13] E. Jahanfekr, M. R. Mohammadizadeh, S. Shojaee, Insight to damage identification in truss-type structures using a second-order gradient-based algorithm, Iranian Journal of Science and Technology, Transactions of Civil Engineering, 45(4) (2021), 2145-2175.

[14] S. F. Jiang, C. M. Zhang, S. Zhang, Two-stage structural damage detection using fuzzy neural networks and data fusion techniques, Expert Systems with Applications, 38(1) (2011), 511-519.

[15] A. Kaveh, S. M. Javadi, M. Maniat, Damage assessment via modal data with a mixed particle swarm strategy, ray optimizer, and harmony search, Asian Journal of Civil Engineering, 15(1) (2014), 95-106.

[16] S. S. Kourehli, M. K. Chehre, A. G. Zamani, Prediction of structural damage location with adaptive neuro-fuzzy inferential system, Iranian Journal of Structural Engineering, 3 (2016), 61-71.

[17] D. P. Kumar, V. Muralidharan, S. Ravikumar, Histogram as features for fault detection of multi point cutting tool-A data driven approach, Applied Acoustics, 186 (2022). DOI:10.1016/j.apacoust.2021.
108456.

[18] X. Li, S. Law, Adaptive Tikhonov regularization for damage detection based on nonlinear model updating, Mechanical Systems and Signal Processing, 24(6) (2010), 1646-1664.

[19] D. W. Marquardt, An algorithm for least-squares estimation of nonlinear parameters, Journal of the Society for Industrial and Applied Mathematics, 11(2) (1963), 431-441.

[20] S. Mazzoni, F. Mc Kenna, M. H. Scott, G. L. Fenves, B. Jeremic, Open system for earthquake engineering simulation (OpenSees), University of California Berkeley USA, 2003.

[21] M. T. H. M. Menhaj, Training feedforward networks with the Marquardt algorithm, IEEE Transactions on Neural Networks, 5(6) (1994), 989-993.

[22] A. Mojtahedi, M. A. Lotfollahi, Y. Hassanzadeha, M. M. Ettefagh, M. H. Aminfara, A. B. Aghdam, Developing a robust SHM method for offshore jacket platform using model updating and fuzzy logic system, Applied Ocean Research, 33(4) (2011), 398-411.

[23] J. A. Momoh, X. W. Ma, K. Tomsovic, Overview and literature survey of fuzzy set theory in power system, IEEE Transactions on Power Systems, 10(3) (1995), 1676-1690.

[24] M. A. Oliveira, D. J. Inman, Performance analysis of simplified fuzzy ARTMAP and probabilistic neural networks for identifying structural damage growth, Applied Soft Computing Journal, 52(C) (2017), 53-63.

[25] D. R. Parhi, K. M. Manoj, S. Chinmaya, Prediction of cracks using FEA analysis and fuzzy logic approach, International Journal of Artificial Intelligence and Computational Research (I J A I C R), 4(1) (2012), 13-20.

[26] D. R. Parhi, C. Sasanka, Smart crack detection of a cracked cantilever beam using fuzzy logic technology with hybrid membership functions, Engineering and Technology Research, 3(8) (2011), 270-278.

[27] M. P. Prashant, G. Ranjan, Genetic fuzzy system for damage detection in beams and helicopter rotor blades, Computer Methods Applied Mechanics Engineering, 192(16-18) (2003), 2031-2057.

[28] G. Ranjan, A fuzzy logic system for ground based structural health monitoring of a helicopter rotor using modal data, Journal of Intelligent Material Systems and Structures, 12(6) (2001), 397-407.

[29] G. Ranjan, Fuzzy cognitive maps for structural damage detection, Fuzzy Cognitive Maps for Applied Sciences and Engineering, 54 (2014), 267-290.

[30] T. M. Reda, J. Lucero, Damage identification for structural health monitoring using fuzzy pattern recognition, Engineering Structures, 27(12) (2005), 1774-1783.

[31] S. Sahu, B. P. Kumar, D. R. Parhi, Intelligent hybrid fuzzy logic system for damage detection of beam-like structural elements, Journal of Theoretical and Applied, 55(2) (2017), 509-521.

[32] D. S. Samuel, D. J. Milton, L. J. Vicente, J. B. Michael, Structural damage detection by fuzzy clustering, Mechanical Systems and Signal Processing, 22(7) (2008), 1636-1649.

[33] J. P. Sawyer, S. S. Rao, Structural damage detection and identification using fuzzy logic, American Institute of Aeronautics and Astronautics Journal, 38(12) (2000), 2328-2335.

[34] R. Sethi, S. K. Senapati, D. R. Parhi, Structural damage detection by fuzzy logic technique, Applied Mechanics and Materials, 592-594 (2014), 1175-1179.

[35] A. Tarighat, Model based damage detection of concrete bridge deck using adaptive neuro-fuzzy inference system, International Journal of Civil Engineering, 11(3) (2013), 170-181.

[36] A. Teughels, J. Maeck, G. D. Roeck, Damage assessment by FE model updating using damage functions, Computers and Structures, 80(25) (2002), 1869-1879.

[37] A. Teughels, G. D. Roeck, Damage detection and parameter identification by finite element model updating, Archives of Computational Methods in Engineering, 12(2) (2005), 123-164.

[38] J. Wang, Q. S. Yang, Modified Tikhonov regularization in model updating for damage identification, Structural Engineering and Mechanics, 44(5) (2012), 585-600.

[39] B. M. Wilamowski, J. D. Irwin, Intelligent systems, Taylor and Francis Group, 2011.

[40] B. M. Wilamowski, H. Yu, Improved computation for Levenberg Marquardt training, IEEE Transactions on Neural Networks, 21(6) (2010), 930-937.

[41] L. A. Zadeh, Fuzzy sets, Information and Control, 8(3) (1965), 338-353.

[42] S. J. Zheng, Z. Q. Li, H. T. Wang, A genetic fuzzy radial basis function neural network for structural health monitoring of composite laminated beams, Expert Systems with Applications, 38(9) (2011), 11837-11842.