An optimized bearing parameter identification approach from vibration response spectra

Mutra, Rajasekhara Reddy; Srinivas, J.

doi:10.21595/jve.2018.20005

Cited by 8 publications

(2 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Taking a hydrodynamic bearing as an instance, its dynamic parameters are functions of speed, eccentric ratio, and other variables so that their estimation usually requires elaborate works. Mutra and Srinivas 30 employed a FEM model for a rotor-bearing system and used an error-based formulation in terms of response amplitudes at bearing nodes to identify the speed-dependent stiffness and damping parameters of a hydrodynamic bearing, in which a modified particle swarm optimization (PSO) with mutation was applied for solution search. The robustness of the methodology was confirmed via introducing different levels of noise into the measured reference signals.…”

Section: Introductionmentioning

confidence: 99%

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

Lin

Liang

Huang

et al. 2023

Advances in Mechanical Engineering

View full text Add to dashboard Cite

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.

show abstract

Section: Introductionmentioning

confidence: 99%

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

Lin

Liang

Huang

et al. 2023

Advances in Mechanical Engineering

View full text Add to dashboard Cite

show abstract

“…It is believed that an accurate assessment of bearing parameters can prove to be a key and significant step before unbalance identification. Mutra and Srinivas [36] established error-based identification with the help of modified particle swarm optimization (PSO) for solution search. They introduced different degrees of noise into the system to verify the robustness of their approach.…”

Section: Introductionmentioning

confidence: 99%

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

Bera,

Huang,

Najibullah

et al. 2023

Machines

View full text Add to dashboard Cite

Rotating machinery is the fundamental component of almost all industrial frameworks. Therefore, prognostics and health management (PHM) have emerged as crucial requirements for effectively managing and sustaining various systems in a timely manner. The unbalanced fault has been recognized as a significant contributing factor in the development of faults in rotor-bearing systems, eventually causing failure. Thus, it is essential to monitor the unbalance and maintain it within acceptable bounds in order to guarantee the system’s proper operation. Most approaches to the rotor’s unbalance monitoring are model-based instead of data-driven due to the shortage of faulted data. In a derived model-based approach, proper identification of the model’s parameters, e.g., bearing parameters, always plays a very crucial role. Nonetheless, the identified model’s parameters in their initial state would inevitably degenerate during a long-term operation because of aging or environmental changes, such that they are no longer well representative of the real system. In this context, this paper offers an adaptive model-based approach for the assessment of unbalance faults developing over days in a rotor-bearing model. The model is adaptive in the sense that it automatically adjusts its parameters so that they are more closely aligned with the real system. A particle swarm optimization (PSO) scheme is utilized in the parameter identification process. The residual serves as the index for initiating the adaptive process when it is greater than a preset percentage. Individual feature errors work as a gauge to determine which bearing parameters need to be reevaluated. A set of 16-month operational data from a local petrochemical company is used to validate the approach. The unbalanced deterioration trend is evaluated, and results from the adaptive methodology are assessed to show its superiority over the original one. It is also observed that the model’s capacity to anticipate unbalance is greatly enhanced by the adaptive strategy. Finally, future unbalances are explored to show its capacity for continuous monitoring-based maintenance solutions.

show abstract

An Adaptative Differential Evolution Algorithm for Vibration Level Reduction in Rotordynamics

Mlaouhi

Guedria

Bouraoui

2021

Lecture Notes in Mechanical Engineering

View full text Add to dashboard Cite

An optimized bearing parameter identification approach from vibration response spectra

Cited by 8 publications

References 37 publications

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

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

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

An Adaptative Differential Evolution Algorithm for Vibration Level Reduction in Rotordynamics

Contact Info

Product

Resources

About