Design and development of 3-stage determination of damage location using Mamdani-Adaptive Genetic-Sugeno model

• Autorzy: Sahu S. , Kumar P. B. , Parhi D. R.

Identyfikatory

DOI

10.15632/jtam-pl.55.4.1325

• Języki publikacji: EN

Abstrakty

EN

Damage detection in structural elements like beams is one of important research areas for health monitoring. Initiation of a fault in the form of a crack or any damage puts a limitation on the service life of a structural member. So, in this paper, a method is proposed which uses the advantages of soft computing techniques like Fuzzy Inference Systems (Mamdani and Sugeno) and Adaptive Genetic Algorithm for three stage refinement of the data base generated using dynamic responses from a cracked fixed-free aluminum alloy beam element. For the crack element reference, a finite element model of a single transverse crack has been considered. The proposed method describes both Mamdani and Sugeno Fuzzy Inference Systems for training of damage parameters. In the Adaptive Genetic Algorithm, a statistics based method has been incorporated to limit the randomness of the search process. Finally, the results from the Mamdani-Adaptive Genetic-Sugeno model (MAS) are validated with the results from the experimental analysis.

Słowa kluczowe

EN

damage; Mamdani FIS; Sugeno FIS; Adaptive Genetic Algorithm; vibration; natural frequencies

• Wydawca: Polskie Towarzystwo Mechaniki Teoretycznej i Stosowanej
• Czasopismo: Journal of Theoretical and Applied Mechanics
• Rocznik: 2017
• Tom: Vol. 55 nr 4
• Strony: 1325--1339
• Opis fizyczny: Bibliogr. 19 poz., rys., tab.

Twórcy

autor

Sahu S.

• School of Mechanical Engineering, Kalinga Institute of Industrial Technology, KIIT University, Bhubaneswar, Odisha, India

autor

Kumar P. B.

• Department of Mechanical Engineering, National Institute of Technology, Rourkela, Odisha, India

autor

Parhi D. R.

• Department of Mechanical Engineering, National Institute of Technology, Rourkela, Odisha, India

Bibliografia

• 1. Chandrashekhar M., Ganguli R., 2009, Structural damage detection using modal curvature and fuzzy logic, Structural Health Monitoring, 8, 4, 267-282
• 2. Dervilis N., Worden K., Cross E.J., 2015, On robust regression analysis as a means of exploring environmental and operational conditions for SHM data, Journal of Sound and Vibration, 347, 279-296
• 3. Fegade P.V., Mahajan J.A., Bhole G.P., 2014, Study on vibration analysis of an uncrack and cracked cantilever beam, International Association of Scientific Innovation and Research, 10, 4, 340-343
• 4. Jaiswal N.G., Pande D.W., 2015, Sensitizing the mode shapes of beam towards damage detection using curvature and wavelet transform, International Journal Of Scientific and Technology Research, 4, 4, 266-272
• 5. Holland J.H., 1992, Genetic algorithms, Scientific American, 66-72
• 6. Khaji N., Mehrjoo M., 2014, Crack detection in a beam with an arbitrary number of transverse cracks using genetic algorithms, Journal of Mechanical Science and Technology, 28, 3, 823-836
• 7. Niezrecki C. (Edit.), 2015, Structural Health Monitoring and Damage Detection, Volume 7, Proceedings of the 33rd IMAC, A Conference and Exposition on Structural Dynamics, ISBN 978-3- -319-15230-1
• 8. Parhi D.R., Choudhury S., 2011, Intelligent fault detection of a cracked cantilever beam using fuzzy logic technology with hybrid membership functions, International Journal of Artificial Intelligence and Computational Research, 3, 1, 9-16
• 9. Pawar P.M., Reddy K.V., Ganguli R., 2007, Damage detection in beams using spatial Fourier analysis and neural networks, Journal of Intelligent Material Systems and Structures, 18, 4, 347-359
• 10. Peng Z.K., Lang Z.Q., Billings S.A., 2007, Crack detection using nonlinear output frequency response functions, Journal of Sound and Vibration, 301, 3, 777-788
• 11. Ranjbaran A., Ranjbaran M., 2013, New finite-element formulation for buckling analysis of cracked structures, Journal of Engineering Mechanics, 140, 5
• 12. 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, 11, 1318-1338
• 13. Shahidi S.G., Nigro M.B., Pakzad S.N., Pan Y., 2015, Structural damage detection and localisation using multivariate regression models and two-sample control statistics, Structure and Infrastructure Engineering, 11, 10
• 14. Thatoi D., Choudhury S., Jena P.K., 2014, Fault diagnosis of beam-like structure using modified fuzzy technique, Advances in Acoustics and Vibration
• 15. 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, 2, 1150-1160
• 16. Verma P., Rathore M., Gupta R., 2013, Vibration control of cantilever beam using fuzzy logic controller, International Journal of Science Engineering and Technology Research, 2, 4, 906-909
• 17. Waghulde K.B., Kumar B., 2014, Vibration analysis of cracked cantilever beam with suitable boundary conditions, International Journal of Innovative Science, Engineering and Technology, 1, 10, 20-24
• 18. Yuan H., Peng C., Lin Q., Zhang B., 2014, Simulation of tensile cracking in earth structures with an adaptive RPIM-FEM coupled method, KSCE Journal of Civil Engineering, 18, 7, 2007-2018
• 19. Zhu F., Wu Y., 2014, A rapid structural damage detection method using integrated ANFIS and interval modeling technique, Applied Soft Computing, 25, 473-484