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Published in: Meccanica 12/2023

18-11-2023

Damage detection in cracked structure rotating under the fluid medium through radial basis function neural network technique

Author: Adik Yadao

Published in: Meccanica | Issue 12/2023

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Abstract

Fault identification in a structure using the vibration characteristics is a quiet smart technique for health monitoring of the structure. In this research, the radial basis function neural network (RBFNN) method has been implemented for damage detection in the rotating circular cross-section cantilever type rotor immersed in the altered viscous medium environment as an inverse method. The proposed radial basis function neural network (RBFNN) Controller has five inputs and four outputs parameters. The database for neural network has been developed from the experimental, theoretical and FEM analysis. Theoretical expression has been developed to estimate the influence of the position and size of damage on the vibration characteristics. For damage cantilever rotor with extra mass at the free end, the vibration signature is computed in both transversal directions using the influence coefficient approaches. The effect of fluid forces is obtained by using the Navier–Stokes equation. Local stiffness has been calculated to employ the Strain energy release rate at the crack region of the rotor shaft. The finite element software ANSYS is used for numerical analysis on cracked and non-cracked cantilever rotor shaft to estimate the vibration signature such as natural frequencies and amplitude of Vibration. To determine the robustness of the proposed neural network technique, the computed results from the radial basis function neural network (RBFNN) controller are validated with the experimental, theoretical, and FEM analyses results.

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Metadata
Title
Damage detection in cracked structure rotating under the fluid medium through radial basis function neural network technique
Author
Adik Yadao
Publication date
18-11-2023
Publisher
Springer Netherlands
Published in
Meccanica / Issue 12/2023
Print ISSN: 0025-6455
Electronic ISSN: 1572-9648
DOI
https://doi.org/10.1007/s11012-023-01733-7

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