Eventos Anais de eventos
COBEM 2023
27th International Congress of Mechanical Engineering
Virtual Sensing of Rotating Machines using Augmented Kalman Filter and ROSS Models
Submission Author:
Stanley Washington Ferreira Rezende , GO
Co-Authors:
Stanley Washington Ferreira Rezende, Raimundo Neto, Maria Carolina Albuquerque de Souza Santos, Jose dos Reis Vieira de Moura Jr, Aldemir Ap Cavalini Jr, Valder Steffen Jr
Presenter: Stanley Washington Ferreira Rezende
doi://10.26678/ABCM.COBEM2023.COB2023-0674
Abstract
Rotating machines play a fundamental role in the industry due to their adaptability and usability. However, they are susceptible to noise and vibrations, damaging equipment and production processes. Continuously monitoring and modeling rotors is crucial to ensure optimal performance. Despite this, physical methods for measuring vibration responses can be limited by the high cost of sensors, restricted access to the monitoring area, and geometric complexity, making it expensive or impossible to measure vibration responses of certain degrees of freedom (DOFs) in rotating machines. As a solution, virtual sensing (VS) techniques combine information from accurate models with monitoring data to produce more precise predictions. In this work, the Augmented Kalman Filter (AKF) method is used to estimate the vibrational response of a rotor, including unmeasurable DOFs. The AKF algorithm combines information from a finite element model with numerically vibration data to produce more accurate estimates. A Differential Evolution-based optimization process is incorporated to facilitate the execution of the AKF. The methodology can estimate vibrational behavior with a maximum error of ≤11μm, given a prediction delay, and quickly achieves stability with an error of ≤2μm at a low computational cost, which is important for the field of study.
Keywords
Virtual Sensing, Kalman Filter, Rotordynamics, Vibration Analysis
