A novel model-based unbalance monitoring and prognostics for rotor-bearing systems is introduced in the paper. An analytical method is first applied for rotor modeling and the calculated first natural frequency is validated by an FEM model. The rotor-bearing model with the identified bearing parameters is next validated with an operational 3-stage turbine-bearing’s machine on the first critical speed. The novelty of the approach is that the unbalance proceeding with optimization schemes is evaluated in two phases. In phase I, the bearing parameters and the initial unbalances are simultaneously evaluated based on the operational data soon after an overhaul. In phase II, the unbalance deterioration with time is identified through every day’s measured vibration at two bearings. A set of operational data over 16 months, provided by a local company, are used to test the approach. The evaluated unbalance deterioration trend is verified by the collaborated company from two consecutive overhauls. Five optimization algorithms are also tested and the results prove the robustness of the derived approach. Finally, the unbalance forecasting capability extrapolating from historical unbalance curve is demonstrated and that can work as prognostics in a condition-based maintenance strategy.
- Online real-time rotor unbalance monitoring
- model-based unbalance monitoring
- parameter identification for rotor-bearing
- unbalance diagnostics and prognostics of rotor-bearing
- unbalance forecasting