Estimation the Natural Frequencies of a Cracked Shaft Based on Finite Element Modeling and Artificial Neural Network
The early detection of faults in rotating systems considers an integral approach that has received considerable attention from the industrial sector, as it contributes to preventing catastrophic failures in machines. In this research, the natural frequencies of a shaft, when it is healthy and when cracks with different depths are introduced, have been calculated. The deviation of the computed natural frequencies from the healthy ones is counted as a sign of the presence of an abnormality in the system. For this intention, the finite element analysis (FEA) method based on ANSYS software has been utilized to obtain the first five natural frequencies of the shaft when there is a crack of different severity at different positions. The results of the FEA are used for designing an artificial neural network (ANN) model that can be easily used to predict the first five natural frequencies of the shaft based on just the crack’s position and depth. Finally, the predicted natural frequencies by the deigned ANN have been compared to their peers that were computed using the FEA method. The absolute error percentage has then been calculated and used to get an indication of how close the result of both techniques is. The recorded highest error percentage was 0.67%, which is quite small and referring to that the designed ANN can accurately predict the natural frequencies of rotating systems.
D. Giardino, M. Matta, and S. Spanò, "A feature extractor IC for Acoustic Emission non-destructive testing," International Journal on Advanced Science, Engineering and Information Technology, vol. 9, pp. 538-543, 2019.
G. C. Cardarilli, L. Di Nunzio, R. Fazzolari, D. Giardino, M. Matta, M. Re, et al., "Acoustic Emissions Detection and Ranging of Cracks in Metal Tanks Using Deep Learning," in Lecture Notes in Electrical Engineering vol. 627, ed, 2020, pp. 325-331.
M. H. H. Shen and J. E. Taylor, "An identification problem for vibrating cracked beams," Journal of Sound and Vibration, vol. 150, pp. 457-484, 1991/11/08/ 1991.
S. Gantasala, J.-C. Luneno, and J.-O. Aidanpää, "Investigating How an Artificial Neural Network Model Can Be Used to Detect Added Mass on a Non-Rotating Beam Using Its Natural Frequencies: A Possible Application for Wind Turbine Blade Ice Detection," Energies, vol. 10, p. 184, 2017.
M. S. Mhaske and S. N. Shelke, "Detection of Depth and Location of Crack in a Beam by Vibration Measurement and its Comparative Validation in ANN and GA," International Journal of Engineering Research, 2015.
M. Dahak, N. Touat, and T. Benkedjouh, "Crack Detection through the Change in the Normalized Frequency Shape," Vibration, vol. 1, pp. 56-68, 2018.
M. A. Al-Shudeifat and E. Butcher, "Identification of the Critical Crack Depths and Locations of Rotordynamic Systems in Backward Whirl," presented at the 7th International Workshop on Structural Health Monitoring: From System Integration to Autonomous Systems, IWSHM, 2009.
G. D. Gounaris, C. A. Papadopoulos, and A. D. Dimarogonas, "Crack identification in beams by coupled response measurements," Computers & Structures, vol. 58, pp. 299-305, 1996/01/17/ 1996.
Y. Alkassar, "Modal Analysis and Neural Network for Fault Diagnosis in Cracked Clamped Beam," Romanian Journal of Acoustics and Vibration, vol. 13, 2016.
G. D. Gounaris and C. A. Papadopoulos, "Crack identification in rotating shafts by coupled response measurements," Engineering Fracture Mechanics, vol. 69, pp. 339-352, 2002/02/01/ 2002.
S. Orhan, M. Lüy, M. H. Dirikolu, and G. Zorlu, "The Effect of Crack Geometry on the Nondestruc- tive Fault Detection in a Composite Beam," International Journal of Acoustics and Vibration, vol. 21, 2016.
A. V. Deokar and V. D. Wakchaure, "Experimental Investigation of Crack Detection in Cantilever Beam Using Natural Frequency as Basic Criterion," presented at the International Conference on Current Trends in Technology (NUiCONE), 2011.
S. Spanò, G. C. Cardarilli, L. Di Nunzio, R. Fazzolari, D. Giardino, M. Matta, et al., "An efficient hardware implementation of reinforcement learning: The q-learning algorithm," IEEE Access, vol. 7, pp. 186340-186351, 2019.
W. M. Ostachowicz and M. Krawczuk, "Coupled torsional and bending vibrations of a rotor with an open crack," Archive of Applied Mechanics, vol. 62, pp. 191-201, 1992/01/01 1992.
M. Kisa and M. Arif Gurel, "Modal analysis of multi-cracked beams with circular cross section," Engineering Fracture Mechanics, vol. 73, pp. 963-977, 2006/05/01/ 2006.
S. Seyedzadeh, F. P. Rahimian, I. Glesk, and M. Roper, "Machine learning for estimation of building energy consumption and performance: a review," Visualization in Engineering, vol. 6, p. 5, 2018/10/02 2018.
S. Zhong and S. O. Oyadiji, "Identification of cracks in beams with auxiliary mass spatial probing by stationary wavelet transform," Journal of Vibration and Acoustics, Transactions of the ASME, vol. 130, 2008.
L. Hamidi, J. B. Piaud, and M. Massoud, "A study of cracks influence on the modal characteristics of rotors," presented at the International Conference on Vibrations in Rotating Machinery, Bath, UK, 1992.
D. Satpute, P. Baviskar, P. Gandhi, M. Chavanke, and T. Aher, "Crack Detection in Cantilever Shaft Beam Using Natural Frequency," in Materials Today: Proceedings, 2017, pp. 1366-1374.
M. R. S. Reddy, B. S. Reddy, V. N. Reddy, and S. Sreenivasulu, "Prediction of Natural Frequency of Laminated Composite Plates Using Artificial Neural Networks," Engineering vol. 4, 2012.
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