Structural Health Monitoring (SHM) is crucial for ensuring the safety and reliability of rotating machinery. In rotating shafts, structural damage can affect force distribution and alter dynamic behavior. This study conducts a preliminary numerical investigation into the application of the inverse Finite Element Method (iFEM) for rotor monitoring. Based on Euler-Bernoulli beam theory, the iFEM algorithm reconstructs the full three-dimensional deformation field through beam elements of inverse fi nite ñth-order formulation. Static and dynamic Finite Element Method (FEM) simulations assess the accuracy of the methodology under varying loading and boundary conditions. To simulate real-world sensor inaccuracies, Gaussian noise is introduced into the strain data, replicating measurement uncertainties typical of Fiber Bragg Grating (FBG) optical sensors. Results demonstrate the strong iFEM capability to capture deformation patterns, paving the way for iFEM real-time monitoring of a rotating shaft. Its sensitivity to structural changes highlights its suitability for predictive maintenance and fault diagnostics in rotating machinery. This study lays the groundwork for further research into iFEM-based SHM implementations, supporting research advancements and new industrial applications.

iFEM Implementation on a Rotating Shaft for Imbalance Identification

Petriconi E.;Lucii L.;Giglio M.;Sbarufatti C.
2025-01-01

Abstract

Structural Health Monitoring (SHM) is crucial for ensuring the safety and reliability of rotating machinery. In rotating shafts, structural damage can affect force distribution and alter dynamic behavior. This study conducts a preliminary numerical investigation into the application of the inverse Finite Element Method (iFEM) for rotor monitoring. Based on Euler-Bernoulli beam theory, the iFEM algorithm reconstructs the full three-dimensional deformation field through beam elements of inverse fi nite ñth-order formulation. Static and dynamic Finite Element Method (FEM) simulations assess the accuracy of the methodology under varying loading and boundary conditions. To simulate real-world sensor inaccuracies, Gaussian noise is introduced into the strain data, replicating measurement uncertainties typical of Fiber Bragg Grating (FBG) optical sensors. Results demonstrate the strong iFEM capability to capture deformation patterns, paving the way for iFEM real-time monitoring of a rotating shaft. Its sensitivity to structural changes highlights its suitability for predictive maintenance and fault diagnostics in rotating machinery. This study lays the groundwork for further research into iFEM-based SHM implementations, supporting research advancements and new industrial applications.
2025
Structural Health Monitoring 2025: Ensuring Mobility and Autonomy with Sustainability - Proceedings of the 15th International Workshop on Structural Health Monitoring, IWSHM 2025
9781605956992
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/1311239
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