Journal of Applied and Computational Mechanics

Analysis and Recognition of Standards in Intelligent Hybrid ‎Systems using Natural Computing

Document Type : Research Paper

Authors

1 UNESP - Univ. Estadual Paulista, Faculty of Engineering of Ilha Solteira, Department of Mechanical Engineering, Ilha Solteira, 15385-000, Brasil

2 Faculty of Technology of Araçatuba, Department of Biofuels, Av. Prestes Maia, 1764 - Ipanema, Araçatuba, 16052-045, Brasil

3 UNESP - Instituto de Química, Departamento de Engenharia, Física e Matemática, Rua Prof. Francisco Degni, 55 – Quitandinha, Araraquara, 14800-060, Brasil

4 UNESP - Univ. Estadual Paulista, Faculty of Engineering of Ilha Solteira, Department of Mechanical Engineering, Ilha Solteira, 15385-000, Brasil‎

Abstract

This work shows the application of one of the techniques of bioengineering, the perceptron network in the detection of system failures, and also allows the use of the perceptron network technique in choosing the location of the best sensor to be used in the dynamic system. The application of the perceptron network was adopted because it is considered the best binary linear classifier. This work is considered multidisciplinary and difficult to develop. The final result demonstrates a severe application of pre-processing and processing, until the classification and grouping of signals in the two phases of the work. Through the results found, this work can be considered successful and can be applied in several areas of engineering for structural analysis.

Keywords

Main Subjects

Publisher’s Note Shahid Chamran University of Ahvaz remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

[1] Popkova, E.G., Ragulina, Y.V., Bogoviz, A.V., Industry 4.0: Industrial: Revolution of the 21st Century, Springer International, Gewerbestrasse, 2019.
[2] Manzinni, R., Regattieri, A., Pham, H., Ferrari, E., Maintenance for Industrial Systems, Springer-Verlag, London, 2010.
[3] Levitt, J., Complete Guide to Preventive and Predictive Maintenance, Industrial Press Inc., New York, 2010.
[4] Bauchau, O.A., Craig, J.I., Structural Analysis: With Applications to Aerospace Structures, Springer Science, London, 2009.
[5] Outa, R., Chavarette, F.R., Mishra, V.N., Goncalves, A.C., Roefero, L.G.P., Moro, T.C., Prognosis and fail detection in a dynamic rotor using artificial immunological system, Engineering Computations, 37(9), 2020, 3127-3145.
[6] Merizio, I.F., Chavarette, F.R., Moro, T.C., Outa, R., Mishra, V.N., Machine Learning Applied in the Detection of Faults in Pipes by Acoustic Means, Journal of The Institution of Engineers (India): Series C, 2021, DOI: 10.1007/s40032-021-00682-y.
[7] Outa, R., Chavarette, F.R., Toro, P.F., Goncalves, A.C., Santos, L.H., Prognosis and Detection of Experimental Failures in Open Field Diesel Engines Applying Wieners Artificial Immunological Systems, Journal of Applied and Computational Mechanics, 7(1), 2021, 1-12.
[8] Martynyuk, A., Radziszewski, B., Szadkowski, A., Dynamics and Stability of Motion of Shock and Hybrid Systems, Sciendo de Gruyter Company, German, 2019.
[9] Melin, P., Castillo, O., Hybrid Intelligent Systems for Pattern Recognition Using Soft Computing: An Evolutionary Approach for Neural Networks and Fuzzy Systems, Springer-Verlag, Heidelberg, 2005.
[10] Khosla, R., Dillon, T., Engineering Intelligent Hybrid Multi-Agent Systems, Springer Science, New York, 1997.
[11] Wermter, S., Sun, R., Hybrid Neural Systems, Springer-Verlag, Heidelberg, 2000.
[12] Wang, D., Yu, M., Low, C.B., Arogeti, S., Model-based Health Monitoring of Hybrid Systems, Springer Science, New York, 2013.
[13] Boller, C., Chang, F.K., Fujino, Y., Encyclopedia of Structural Health Monitoring, Wiley & Sons, Southern Gate, 2009.
[14] Balageas, D., Fritzen, C.P., Güemes, A., Structural Health Monitoring, ISTE, London, 2006.
[15] Liang, Z., Lee, G.C., Random Vibration: Mechanical, Structural, and Earthquake Engineering Applications, CRC Press, Boca Raton, 2015.
[16] Crandall, S.H., Mark, W.D., Random Vibration in Mechanical Systems, Academic Press, London, 1963.
[17] Newland, D.E., An Introduction to Random Vibrations, Spectral & Wavelet Analysis, Dover Publications, New York, 1975.
[18] Wang, L., Fu, X., Data Mining with Computational Intelligence, Springer-Verlag, Heidelberg, 2005.
[19] Han, J., Kamber, M., Pei, J., Data Mining Concepts and Techniques, Elsevier, Waltham, 2012.
[20] Ye, N., The Handbook of Data Mining, Lawrence Erlbaum Associates, Mahwah, 2003.
[21] Bigus, J.P., Data Mining with Neural Networks: Solving Business Problems from Application Development to Decision Support, MacGraw-Hill Companies, New York, 1996.
[22] Felici, G., Vercellis, C., Mathematical Methods for Knowledge Discovery and Data Mining, IGI Global, Convent Garden, 2008.
[23] Da Silva, I.N., Spatti, D.H., Flauzino, R.A., Liboni, L.H.B., Alves, S.F.R., Artificial Neural Networks: A Pratical Course, Springer International, Switzerland, 2017.
Journal of Applied and Computational Mechanics
Volume 7, Issue 3
July 2021
Pages 1764-1773