PSI - Issue 39

Nabam Teyi et al. / Procedia Structural Integrity 39 (2022) 608–623 Author name / Structural Integrity Procedia 00 (2019) 000–000

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Analysis, 81, 270–293. https://doi.org/10.1016/j.engfailanal.2017.07.011 Bansode V,M., Billore M., 2021. Crack Detection In A Rotary Shaft Analytical And Experimental Analyses: A Review, Materials Today: Proceedings, 47, 6301–6305. https://doi.org/10.1016/j.matpr.2021.05.536 Thatoi D.N., Das H.C., Parhi D.R., 2012. Review of Techniques for Fault Diagnosis in Damaged Structure and Engineering System, Advances in Mechanical Engineering, 2012, 12 pages. doi:10.1155/2012/327569 Saridakis K.M., Chasalevris A.C., Papadopoulos C.A., Dentsoras A.J., 2008. Applying Neural Networks, Genetic Algorithms And Fuzzy Logic For The Identification Of Cracks In Shafts By Using Coupled Response Measurements, Computers and Structures, 86, 1318–1338. doi:10.1016/j.compstruc.2007.08.004 Baviskar P. R., Tungikar V. B., 2013. Experimental Investigations on Crack Detection Using Modal Analysis and Prediction of Properties for Multiple Cracks by Neural Network, J. Inst. Eng. India Ser. C, 94, 299–306. DOI 10.1007/s40032-013-088-7 Baviskar PR., Tungikar V. B., 2014. Experimental Verification Of Detection And Prediction Of Multiple Cracks By Vibrations, FEM And ANN, Multidiscipline Modeling in Materials and Structures, 3, 290–303. DOI 10.1108/MMMS-06-2013-0040 Kankar P.K., Sharma S.C., Harsha S.P., 2012. Vibration-Based Fault Diagnosis Of A Rotor Bearing System Using Artificial Neural Network And Support Vector Machine, Int. J. Modelling, Identification and Control, 15, 185–198. doi/abs/10.1504/IJMIC.2012.045691 Sahin M., Shenoi R.A., 2003. Quantification And Localisation Of Damage In Beam-Like Structures By Using Artificial Neural Networks With Experimental Validation, Engineering Structures, 25, 1785–1802. doi:10.1016/j.engstruct.2003.08.001 Sahin M., Shenoi R.A., 2003. Vibration-Based Damage Identication In Beam-Like Composite Laminates By Using Artificial Neural Networks, Proc. Instn M ech. Engrs Part C, 217, 661–676. DOI: 10.1243/095440603321919581 Gómez M.J., Castejón C., García-Prada J.C., 2016. Automatic Condition Monitoring System For Crack Detection In Rotating Machinery, Reliability Engineering & System Safety, 152, 239–247. https://doi.org/10.1016/j.ress.2016.03.013 Castejon C., Garcıa -Prada J.C., Gomez M.J., Meneses J., 2014. Automatic Detection Of Cracked Rotors Combining Multiresolution Analysis And Artificial Neural Networks, Journal of Vibration and Control, 1–14. DOI: 10.1177/1077546313518816 Behera S.K., Parhi D.R., Das H.C., 2018. Experimental A Hybrid Intelligent Model For Crack Diagnosis In A Free-Free Aluminium Beam Structure, Modelling, Measurement and Control B, 87, 68–77. https://doi.org/10.18280/mmc_b.870202 Jena P.C., 2018. Fault Assessment of FRC Cracked Beam by using Neuro-Fuzzy Hybrid Technique, Materials Today: Proceedings, 5, 19216– 19223. https://doi.org/10.1016/j.matpr.2018.06.277 Talekar V.V., Bhanuse V.R., Kulkarni J.V., 2016. Vibration Analysis of Cracked Beam Using Fuzzy Logic Technique, 2016 International Conference on Automatic Control and Dynamic Optimization Techniques (ICACDOT), 2016, pp. 678-682. 10.1109/ICACDOT.2016.7877672 Gowd B.P., Jayasree K., Hegde M.N., 2018. Comparison Of Artificial Neural Networks And Fuzzy Logic Approaches For Crack Detection In A Beam Like Structure, International Journal of Artificial Intelligence and Applications. 9, 35–52. DOI : 10.5121/ijaia.2018.9103 He Y., Guo D., Chu F., 2001. Using Genetic Algorithms And Finite Element Methods To Detect Shaft Crack For Rotor-Bearing System, Mathematics and Computers in Simulation. 57, 95–108. https://doi.org/10.1016/S0378-4754(01)00295-6 Saridakis K.M., Chasalevris A.C., Dentsoras A.J., Papadopoulos C.A., 2006. Fusing Neural Networks, Genetic Algorithms And Fuzzy Logic For Diagnosis Of Cracks In Shafts. Intelligent Production Machines and Systems. 332–337. https://doi.org/10.1016/B978-008045157-2/50061 4 Xiang J., Zhong Y., Chen X., He Z., 2008. Crack Detection In A Shaft By Combination Of Wavelet-Based Elements And Genetic Algorithm. International Journal of Solids and Structures. 45, 4782–4795. doi:10.1016/j.ijsolstr.2008.04.014 Vakil-Baghmisheh M.T., Peimani M., Sadeghi M.H., Ettefagh M.M., 2008. Crack Detection In Beam-Like Structures Using Genetic Algorithms. Applied Soft Computing. 8, 1150–1160. doi:10.1016/j.asoc.2007.10.003 Agarwalla D.K., Khan A.S., Sahoo S.K., 2015. Application of Genetic Fuzzy System for Damage Identification of Cantilever Beam Structure. Procedia Engineering. 144, 215–225. doi:10.1016/j.proeng.2016.05.027 Parhi D.R., Dash A.K., Das H.C., 2015. Formulation of a Genetic Algorithm Based Methodology for Multiple Crack Detection in a Beam Structure. Australian Journal of Structural Engineering. 12:2, 127–139. doi: 10.7158/13287982.2011.11465085 Furukawa T., Yagawa G., 1997. Inelastic Constitutive Parameter Identification Using An Evolutionary Algorithm With Continuous Individuals. International Journal for Numerical Methods in Engineering, 40, 1071–1090. doi/abs/10.1002/(SICI)1097 0207(19970330)40:6<1071::AID-NME99>3.0.CO;2-8 Khatir S., Dekemele K., Loccufier M., Khatir t., Wahab M.A., 2018. Crack Identification Method In Beam-Like Structures Using Changes In Experimentally Measured Frequencies And Particle Swarm Optimization. Comptes Rendus Mecanique. 346, 110–120. https://doi.org/10.1016/j.crme.2017.11.008 Jena P.K., Thatoi D.N., Parhi D.R., 2015. Dynamically Self-Adaptive Fuzzy PSO Technique for Smart Diagnosis of Transverse Crack. Applied Artificial Intelligence: An International Journal. 29:3, 211–232. https://doi: 10.1080/08839514.2015.1004611 Mohan S.C., Maiti D.K., Maity D., 2013. Structural Damage Assessment Using FRF Employing Particle Swarm Optimization. Applied Mathematics and Computation. 219, 10387–10400. https://doi.org/10.1016/j.amc.2013.04.016 Mohan S.C., Yadav A., Maiti D.K., 2014. A Comparative Study On Crack Identification Of Structures From The Changes In Natural Frequencies Using GA And PSO. Engineering Computations: International Journal for Computer-Aided Engineering and Software. 31, 1514–1531. DOI 10.1108/EC-02-2013-0061 Gómez M.J., Castejón C., García-Prada J.C., 2016 Review of Recent Advances in the Application of the Wavelet Transform to Diagnose Cracked Rotors. Algorithms 2016, 9, 19; doi:10.3390/a9010019 Rao K.B., Reddy D.M., 2021. Fault Detection In Rotor System By Discrete Wavelet Neural Network Algorithm. Journal of Vibration and Control. 0. 1–17. https://doi.org/10.1177%2F10775463211030754