PSI - Issue 39
Nabam Teyi et al. / Procedia Structural Integrity 39 (2022) 608–623 Author name / StructuralIntegrity Procedia 00 (2019) 000–000
623
16
Nanda J., Das L.D., Das S., Das H.C., 2014. Influence Of Multi-Transverse Crack On Cantilever Shaft. International Journal of Damage Mechanics. 0, 1–23. DOI: 10.1177/1056789514560916 Nanda J., Das L.D., Das H.C., Biswal A., Tripathy A., 2012. Analysis Of Detecting Multi Crack With Location And Size In Simply Supported Shaft Using ANFIS. Applied Mechanics and Materials. 541–542,649-657. https://doi.org/10.4028/www.scientific.net/AMM.541-542.649 Nanda J., Parhi D.R., 2013. Theoretical Analysis of the Shaft. Advances in Fuzzy Systems. 2013. https://doi.org/10.1155/2013/392470 Das S., Nayak B., Sarangi S.K., Biswal D.K., 2015. Conditioning Monitoring of Robust Damage of Cantilever Shaft using Experimental and Adaptive Neuro-Fuzzy Inference System (ANFIS). Procedia Engineering. 144, 328–335. doi:10.1016/j.proeng.2016.05.140 Shim M.B., Suh M.W., 2002. Crack Identification Using Neuro-Fuzzy-Evolutionary Technique. KSME International Journal. 16, 454–467. https://doi.org/10.1007/BF03185075 ShimM.B., Suh M.W., 2010. A Study on Multiobjective Optimization Technique for Inverse and Crack Identification Problems. Inverse Problems in Engineering. 10, 441–465. https://doi.org/10.1080/1068276021000008504 Shim M.B., Suh M.W., 2003. Crack Identification Of A Planar Frame Structure Based On A Synthetic Artificial Intelligence Technique. Int. J. Numer. Meth. Engng. 57, 57–82. DOI: 10.1002/nme.670 Thada A., Panchal S., Dubey A., Rao L.B., 2021. Machine Learning Based Frequency Modelling. Mechanical Systems and Signal Processing. 160, 107195. https://doi.org/10.1016/j.ymssp.2021.107915 Hein H., Jaanuska L., 2019. Comparison Of Machine Learning Methods For Crack Localization. Acta Et Commentationes Universitatis Tartuensis De Mathematica. 23. https://doi.org/10.12697/ACUTM.2019.23.13 Rodrigues C.E., Junior C.L.N., Rade D.A., 2020. Machine Learning Techniques for Fault Diagnosis of Rotating Machines Using Spectrum Image of Vibration Orbits. DOI: 10.48011/asba.v2i1.1101 Sánchez R.V., Lucero P., Macancela J.C., Alonso H.R., Cerrada M., Cabrera D., Castejón C., 2020. Evaluation of Time and Frequency Condition Indicators from Vibration Signals for Crack Detection in Railway Axles. Appl. Sci. 2020, 10, 4367. doi:10.3390/app10124367 Zhao W., Hua C., Wang D., Dong D., 2021. Fault Diagnosis of Shaft Misalignment and Crack in Rotor System Based on MI-CNN. Proceedings of the 13th International Conference on Damage Assessment of Structures. Lecture Notes in Mechanical Engineering. Springer, Singapore.. https://doi.org/10.1007/978-981-13-8331-1_39 Söffker D., Wei C., Wolff S., Saadawia M.S., 2016. Detection Of Rotor Cracks: Comparison Of An Old Model-Based Approach With A New Signal-Based Approach. Nonlinear Dynamics. 83,1153–1170.DOI 10.1007/s11071-015-2394-5 Choudhury S., Thatoi D.N., Hota J., Rao M.D., 2019. Predicting Crack Through A Well Generalized And Optimal Tree-Based Regressor. International Journal of Structural Integrity. 1757–9864 DOI 10.1108/IJSI-09-2019-0086 Choudhury S., Thatoi D.N., Hota J., Rao M.D., 2019. Predicting Crack Through A Well Generalized And Optimal Tree-Based Regressor. 1757– 9864 DOI 10.1108/IJSI-09-2019-0086 Liu L., Meng G., 2005. Crack Detection in Supported Beams Based on Neural Network and Support Vector Machine. Advances in Neural Networks – ISNN 2005. ISNN 2005. Lecture Notes in Computer Science, vol 3498. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11427469_95 Behera S.K., Parhi D.R., Das H.C., 2018. Approach To Establish A Hybrid Intelligent Model For Crack Diagnosis In A Fix-Hinge Beam Structure. International Journal of Structural Integrity. 1757–9864 DOI 10.1108/IJSI-05-2018-002 Mohammed A.A., Neilson R.D., Deans W.F., MacConnell P., 2014. Crack detection in a rotating shaft using artificial neural networks and PSD characterisation. Meccanica. 49, 255–266. DOI 10.1007/s11012-013-9790-z Outa R., Chavarette F.R., Mishra V.N., Goncalves A.C., Roefero L.G.P., Moro T.C., 2014. Prognosis And Fail Detection In A Dynamic Rotor Using Artificial Immunological System. Engineering Computations, 37, 3127–3145. DOI 10.1108/EC-08-2019-0351 Huo Z., Zhang Y., Zhou Z., Huang J., 2017. Crack Detection in Rotating Shafts Using Wavelet Analysis, Shannon Entropy and Multi-class SVM. Industrial Networks and Intelligent Systems. INISCOM 2017. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 221. Springer, Cham. https://doi.org/10.1007/978-3-319-74176-5_29
Made with FlippingBook Ebook Creator