Estimation of Forcing Function, Mistuning, and Modal Damping in a Bladed Rotor

2020 ◽  
Vol 142 (5) ◽  
Author(s):  
Arjun Singh Chauhan ◽  
Alok Sinha
Keyword(s):  
State Space ◽  
Unscented Kalman Filter ◽  
State Space Model ◽  
Transient Vibration ◽  
Space Model ◽  
Vibration Data ◽  
Forcing Function ◽  
Modal Damping ◽  
Bladed Rotor ◽  
Augmented State

Abstract This paper deals with the estimation of forcing function, modal damping, and mistuned modal stiffnesses in a bladed rotor. Previous research on parameter estimation in a mistuned bladed rotor relies on the knowledge of the forcing function as well as the vibration data. This paper presents two novel approaches. The first approach relies on knowledge of both the forcing and vibration data. The parameters are treated as states of the system and an augmented state space model is created. Unscented Kalman filter (UKF) is then used on the steady-state data to estimate the parameters. The second approach eliminates the dependence on forcing data. Both the forcing and parameters are now treated as states of the system to construct an augmented state space model. UKF is then used on transient vibration data for estimation. Numerical results are presented for a simple model of a mistuned bladed rotor which considers a single mode of vibration per blade.

Estimation of Forcing Function, Mistuning and Modal Damping in a Bladed Rotor

2019 ◽  
Author(s):  
Arjun Singh Chauhan ◽  
Alok Sinha
Keyword(s):  
Kalman Filter ◽  
State Space ◽  
Unscented Kalman Filter ◽  
State Space Model ◽  
Space Model ◽  
Vibration Data ◽  
Forcing Function ◽  
Modal Damping ◽  
Bladed Rotor ◽  
Augmented State

Abstract This paper deals with the estimation of forcing function, modal damping and mistuned modal stiffnesses in a bladed rotor. Previous research on parameter estimation in a mistuned bladed rotor relies on the knowledge of the forcing function as well as the vibration data. This paper presents two novel approaches. The first approach relies on knowledge of both the forcing and vibration data. The parameters are treated as states of the system and an augmented state space model is created. Unscented Kalman Filter is then used on the steady state data to estimate the parameters. The second approach eliminates the dependence on forcing data. Both the forcing and parameters are now treated as states of the system to construct an augmented state space model. Unscented Kalman Filter is then used on transient vibration data for estimation. Numerical results are presented for a simple model of a mistuned bladed rotor which considers a single mode of vibration per blade.

scholarly journals A Novel Real-Time Center of Gravity Estimation Method for Wheel Loaders with Front/Rear-Axle-Independent Electric Driving

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Zhiyu Yang ◽  
Jixin Wang ◽  
Yunwu Han
Keyword(s):  
State Space ◽  
Real Time ◽  
Intelligent Control ◽  
Unscented Kalman Filter ◽  
State Space Model ◽  
Center Of Gravity ◽  
The State ◽  
Space Model ◽  
Electric Driving ◽  
Wheel Loaders

Estimation of the center of gravity (CG) is the basis for intelligent control of the front-and-rear-axis-independent electric driving wheel loaders (FREWLs). This paper presents a novel real-time method for estimating the CG of FREWLs, which is suitable for driving and spading conditions on bumpy roads. A FREWL dynamical model is proposed to set up the state-space model. The CG estimator is used to estimate the longitudinal tire force using the state-space model and the improved square-root unscented Kalman filter (ISR-UKF) algorithm. The simulation and experimental results indicate that this method is suitable for FREWL dynamics and operational characteristics, and the estimated value of CG basically converges to the reference value. Finally, the probable reasons for error occurring in two experiments and the practical challenges of this method are discussed. The research in this paper establishes a partial theoretical basis for intelligent control of construction machinery.

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