Investigation of the Unbalance Estimation for a Double U-Joint Driveshaft Under Misalignment Uncertainty and Decreased Stiffness.

Purpose: The aim of this paper is to identify the unbalance of a specific rotor system like the double U-Joint driveshaft mounted on isotropic bearings and moving foundations. The challenge is to estimate the unbalance parameters of such system from its complex signal by considering parallel misalignment and foundation decreased stiffness as uncertainties. Methods: The equations of motion of the system were governed by finite element model (FEM) that allows related degrees of freedom and supports misaligned configurations and unbalanced loads. The uncertainty quantification was achieved by the Chebyshev inclusion (CI) method. The uncertain response generated a dataset of processed signals to train the chosen proper orthogonal decomposition and radial basis functions (POD-RBF), an estimation tool for low-dimension data. Results: The model's accuracy has been validated by numerical and experimental tests. In general, the procedure has given good accuracy when using the real data of the system. Otherwise, applying a regression for experimental systems with a mathematical-training data has drastically increased the errors by the fact of many uncertain conditions. Conclusion: The estimation results proved that the prediction accuracy is sensitive to the training data. Indeed, although using uncertainty quantification, the estimation of occurred unbalances in simulations and experiments was inaccurate compared to the one using the real-captured reduced data. Finally, the use of the proposed method has slightly improved the accuracy of both analyses. [ABSTRACT FROM AUTHOR]