Stochastic finite element analysis using polynomial chaos on a flexible rotor with contact nonlinearity

Kartheek, Amaroju; Vijayan, Kiran

doi:10.1007/s11071-024-09481-3

Nonlinear Dyn

2024

DOI: 10.1007/s11071-024-09481-3

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Stochastic finite element analysis using polynomial chaos on a flexible rotor with contact nonlinearity

Amaroju Kartheek,

Kiran Vijayan

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2024

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Cited by 2 publications

References 37 publications

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Fractional-Order Modeling and Stochastic Dynamics Analysis of a Nonlinear Rubbing Overhung Rotor System

Zhao,

Wang,

Zhang

et al. 2024

Fractal Fract

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To study the nonlinear dynamic behavior and system stability of a rubbing overhung rotor with viscoelastic and memory-effect damping and random uncertain parameters, this paper introduces a fractional-order modeling and stochastic dynamic analysis method for the nonlinear overhung rotor system with frictional impact faults. Firstly, the dynamic equations of the overhung rotor considering friction effect and fractional damping effect are established based on the transfer matrix method and fractional order derivative. Then, the time-domain response of the fractional-order dynamic equations is solved by combining the Runge–Kutta method and the continuous fractional expansion, and the steady-state response characteristics of different fractional damping are analyzed in the deterministic case. Finally, to analyze the response of the system under the effect of stochastic parameters, the sparse grid-based PCE metamodel of the system response is developed. Statistical moments, probability distributions, and sensitivity indices of the response of stochastic systems are revealed. The results of this paper provide a theoretical basis for efficient and accurate prediction of the stochastic response of nonlinear rubbing overhung rotor systems.

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Fractional-Order Modeling and Stochastic Dynamics Analysis of a Nonlinear Rubbing Overhung Rotor System

Zhao,

Wang,

Zhang

et al. 2024

Fractal Fract

View full text Add to dashboard Cite

show abstract

Bayesian Architecture for Predictive Monitoring of Unbalance Faults in a Turbine Rotor–Bearing System

Bera,

Huang,

Lin

et al. 2024

Sensors

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Unbalance faults are among the common causes of interruptions and unexpected failures in rotary systems. Therefore, monitoring unbalance faults is essential for predictive maintenance. While conventional time-invariant mathematical models can assess the impact of these faults, they often rely on proper assumptions of system factors like bearing stiffness and damping characteristics. In reality, continuous high-speed operation and environmental factors like load variations cause these parameters to change. This work presents a novel architecture for unbalance fault monitoring and prognosis, in which the bearing parameters are treated as variables that change with operating conditions. This enables the development of a more reliable mathematical model for continuous monitoring and prognosis of unbalance faults in rotor systems. This Bayesian inference framework uses Markov Chain Monte Carlo (MCMC) sampling to identify dynamic bearing parameters. Specifically, the Metropolis algorithm is employed to systematically evaluate the range of acceptable parameter values within the framework. A novel dual-MCMC loops explore and assess the parameter space, resulting in more accurate and reliable bearing parameter estimations. These updated parameters improve the demonstrated turbine rotor–bearing system’s unbalance assessment up to 74.48% of the residual error compared to models with fixed parameters. This validates the Bayesian framework for predictive monitoring and maintenance-oriented solutions.

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Stochastic finite element analysis using polynomial chaos on a flexible rotor with contact nonlinearity

Cited by 2 publications

References 37 publications

Fractional-Order Modeling and Stochastic Dynamics Analysis of a Nonlinear Rubbing Overhung Rotor System

Fractional-Order Modeling and Stochastic Dynamics Analysis of a Nonlinear Rubbing Overhung Rotor System

Bayesian Architecture for Predictive Monitoring of Unbalance Faults in a Turbine Rotor–Bearing System

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