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Commenced in January 2007 Frequency: Monthly Edition: International Publications Count: 30663


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10011347
Classifying Turbomachinery Blade Mode Shapes Using Artificial Neural Networks
Abstract:
Currently, extensive signal analysis is performed in order to evaluate structural health of turbomachinery blades. This approach is affected by constraints of time and the availability of qualified personnel. Thus, new approaches to blade dynamics identification that provide faster and more accurate results are sought after. Generally, modal analysis is employed in acquiring dynamic properties of a vibrating turbomachinery blade and is widely adopted in condition monitoring of blades. The analysis provides useful information on the different modes of vibration and natural frequencies by exploring different shapes that can be taken up during vibration since all mode shapes have their corresponding natural frequencies. Experimental modal testing and finite element analysis are the traditional methods used to evaluate mode shapes with limited application to real live scenario to facilitate a robust condition monitoring scheme. For a real time mode shape evaluation, rapid evaluation and low computational cost is required and traditional techniques are unsuitable. In this study, artificial neural network is developed to evaluate the mode shape of a lab scale rotating blade assembly by using result from finite element modal analysis as training data. The network performance evaluation shows that artificial neural network (ANN) is capable of mapping the correlation between natural frequencies and mode shapes. This is achieved without the need of extensive signal analysis. The approach offers advantage from the perspective that the network is able to classify mode shapes and can be employed in real time including simplicity in implementation and accuracy of the prediction. The work paves the way for further development of robust condition monitoring system that incorporates real time mode shape evaluation.
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References:

[1] Oberholster, A. J. & Heyns, P. S., 2009. Online condition monitoring of axial floe turbomachinery blades using rotor-axial Eulerian laser Doppler vibrometry. Mechanical Systems and Signal Processing, 23(5), pp. 1634-1643.
[2] Duda O, R., Hart, P. E. & Stork, D. G., 2012. Pattern classification. 2nd ed. s.l.:John Wiley & Sons.
[3] Jeong, H., Park, S., Woo, S. & Lee, S., 2016. Rotating Machinery Diagnostics using Deep Learning on Orbit Plot Images. Procedia Manufacturing, Volume 5, p. 1107–1118.
[4] Pickering, T. M., 2014. Methods for Validation of a Turbomachinery Rotor blade Tip Timing System. Master Thesis ed. Virginia: Virginia Polytechnic Institute and State University.
[5] Mathworks, 2019. Classify Patterns with a Shallow Neural Network. (Online) Available at: https://www.mathworks.com/help/deeplearning/gs/classify-patterns-with-a-neural-network.html (Accessed 25 July 2019).
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