A novel model-based unbalance monitoring and prognostics for rotor-bearing systems

Abstract

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.

An Adaptive Model-Based Approach to the Diagnosis and Prognosis of Rotor-Bearing Unbalance

“…The rotor-bearing system elucidated by Lin et al [19] is portrayed and comprehensively expounded in the illustrative depiction of Figure 1. This visualization encapsulates the intricate geometrical specifications and precise coordinates inherent to the system.…”

“…The entries of matrices include the geometric and material properties of the shaft and disks, bearings' stiffness, and damping constants. They can be described as follows [19].…”

“…Nonetheless, it is crucial to recognize that a diagnostic process reliant solely on measured data has its constraints, predominantly confined to the qualitative assessment of symptoms associated with rotor faults. Achieving precise quantification of these faults, encompassing aspects like the location and extent of unbalance, typically necessitates extensive plant testing, an endeavor that may prove exceedingly time-consuming or, in some cases, unfeasible [19].…”

“…Optimization strategies that are linked with the measured response obtained from test rigs or actual operational machines are typically used to identify model parameters. Lin et al [19] and Lees et al [5] provided a comprehensive analysis of the application of model-based fault identification.…”

“…In view of that, the present paper is an extension of the previous work conducted in [19]. In the aforementioned study, unbalance identification was conducted in two phases, where phase I identified the bearing parameters and initial unbalances.…”