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

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.