Validation of Results Obtained from Different Types of Fuzzy Controllers for Diagnosis of Inclined Edge Crack in Cantilever Beam by Vibration Parameters

Ranjan K. Behera^1, and Dayal R. Parhi¹

¹Department of Mechanical Engineering, National Institute of Technology, Rourkela, Odisha, India

Cite this paper:
Ranjan K. Behera and Dayal R. Parhi. Validation of Results Obtained from Different Types of Fuzzy Controllers for Diagnosis of Inclined Edge Crack in Cantilever Beam by Vibration Parameters. Journal of Mechanical Design and Vibration. 2014; 2(3):63-68. doi: 10.12691/jmdv-2-3-2

Abstract

In this paper, the crack diagnosis using intelligent techniques (using membership functions in different fuzzy controllers) have been developed for inverse investigation of the vibration parameters (like modal frequencies and mode shapes) and crack parameters (like crack location, crack depth and crack inclination) of an inclined edge crack cantilever beam. The vibration parameters are calculated from finite element (using ANSYS) and experimental analysis which are used as inputs to the different fuzzy controllers. The different fuzzy controllers are designed by taking several types of membership functions to calculate the crack parameters. The calculated first three modal frequencies and mode shapes are used to generate the number of fuzzy rules with three output crack parameters. Finally, the proposed intelligent techniques are validated by comparing the results obtained from both FEA and experimental analysis. All the results are obtained from fuzzy controllers are in good agreement with experimental results.

Keywords:
vibration parameters crack parameters inclined edge crack FEA Fuzzy ANSYS

This work is licensed under a Creative Commons Attribution 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

References:

[1]	H.W. Shih, D.P. Thambiratnam and T.H.T. Chan, “Vibration based structural damage detection in flexural members using multi-criteria approach,” Journal of Sound and Vibration, vol. 323, pp. 645–661, 2009.J. Clerk Maxwell, A Treatise on Electricity and Magnetism, 3rd ed., vol. 2. Oxford: Clarendon, 1892, pp.68-73.
[2]	RS Prasad, SC Roy and KP Tyagi, “Effect of Crack Position along Vibrating Cantilever Beam on Crack Growth Rate,” International Journal of Engineering Science and Technology, Vol. 2(5), pp. 837-839, 2010.
[3]	K. Mazanoglu and M. Sabuncu, “A frequency based algorithm for identification of single and double cracked beams via a statistical approach used in experiment,” Mechanical Systems and Signal Processing, vol. 30, pp. 168-185, 2012.
[4]	B. P. NANDWANA and S. K. MAITI, “Modelling Of Vibration Of Beam In Presence Of Inclined Edge Or Internal Crack For Its Possible Detection Based On Frequency Measurements,” Engineering Fracture Mechanics, Vol. 58, No. 3, pp. 193-205, 1997.
[5]	Aysha Kalanad and B. N. Rao, “Detection of crack location and size in structures using improved damaged finite element,” IOP Conf. Series: Materials Science and Engineering, vol. 10, pp. 012054, 2010.
[6]	LI Bing, CHEN Xuefeng, and HE Zhengjia, “Three­Steps­Meshing based Multiple Crack Identification for Structures and Its Experimental Studies,” Chinese journal of mechanical engineering, vol. 26, no. 1, pp. 1-7, 2013.
[7]	Y. M. Kim, C. K. Kim, and G. H. Hong, “Fuzzy set based crack diagnosis system for reinforced concrete structures,”Comput-ers and Structures, vol. 85, no. 23-24, pp. 1828-1844, 2007.
[8]	N. Saravanan, V. N. S. K. Siddabattuni, and K. I. Ramachandran, “Fault diagnosis of spur bevel gear box using artificial neural network (ANN), and proximal support vector machine (PSVM),”Applied Soft Computing Journal,vol.10, no.1, pp. 344-360, 2010.
[9]	T. Boutros and M. Liang, “Mechanical fault detection using fuzzy index fusion,”International Journal of Machine Tools and Manufacture, vol. 47, no. 11, pp. 1702-1714, 2007.
[10]	V. Sugumaran and K. I. Ramachandran, “Fault diagnosis of roller bearing using fuzzy classifier and histogram features with focus on automatic rule learning,”Expert Systems with Applications, vol. 38, no. 5, pp. 4901-4907, 2011.
[11]	L.J.De Miguel and L.F.Bl´ azquez, “Fuzzy logic-based deci-sion-making for fault diagnosis in a DC motor,”Engineering Applications of Artificial Intelligence, vol. 18, no. 4, pp. 423-450, 2005.
[12]	ANSYS Release 12, Inc. engineering simulation software.