Theoretical and experimental studies to predict vibration responses of defects in spherical roller bearings using dimension theory

Kumbhar, Surajkumar G.; Sudhagar, P. Edwin; Desavale, R. G.

doi:10.1016/j.measurement.2020.107846

Cited by 28 publications

(5 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…All the models predicted the data with almost 93% accuracy, except for the 3rd-order model. This is very close to what was published in [ 26 ], where the vibration amplitude variations were predicted with 95% accuracy.…”

Section: Discussionsupporting

confidence: 91%

See 1 more Smart Citation

Non-Linear Regression Models with Vibration Amplitude Optimization Algorithms in a Microturbine

Rodríguez-Abreo

Rodríguez‐Reséndiz

Montoya-Santiyanes

et al. 2021

Sensors

View full text Add to dashboard Cite

Machinery condition monitoring and failure analysis is an engineering problem to pay attention to among all those being studied. Excessive vibration in a rotating system can damage the system and cannot be ignored. One option to prevent vibrations in a system is through preparation for them with a model. The accuracy of the model depends mainly on the type of model and the fitting that is attained. The non-linear model parameters can be complex to fit. Therefore, artificial intelligence is an option for performing this tuning. Within evolutionary computation, there are many optimization and tuning algorithms, the best known being genetic algorithms, but they contain many specific parameters. That is why algorithms such as the gray wolf optimizer (GWO) are alternatives for this tuning. There is a small number of mechanical applications in which the GWO algorithm has been implemented. Therefore, the GWO algorithm was used to fit non-linear regression models for vibration amplitude measurements in the radial direction in relation to the rotational frequency in a gas microturbine without considering temperature effects. RMSE and R2 were used as evaluation criteria. The results showed good agreement concerning the statistical analysis. The 2nd and 4th-order models, and the Gaussian and sinusoidal models, improved the fit. All models evaluated predicted the data with a high coefficient of determination (85–93%); the RMSE was between 0.19 and 0.22 for the worst proposed model. The proposed methodology can be used to optimize the estimated models with statistical tools.

show abstract

Section: Discussionsupporting

confidence: 91%

“…A total of 10 reps were performed for each frequency. The established frequencies were similar to those used in Kumbhar et al [ 26 ], although they mainly focused on bearings.…”

Section: Methodsmentioning

confidence: 80%

Non-Linear Regression Models with Vibration Amplitude Optimization Algorithms in a Microturbine

Rodríguez-Abreo

Rodríguez‐Reséndiz

Montoya-Santiyanes

et al. 2021

Sensors

View full text Add to dashboard Cite

show abstract

“…Therefore, it was decided to use the nominal frequencies of 27, 76, and 127 Hz, equivalent to 1620, 4560, 7620 rpm, respectively, representing different levels of this factor. The frequencies used were similar to those used in Surajkumar Kumbhar et al [22] although these authors mainly focused on bearings.…”

Section: Methodsmentioning

confidence: 99%

Metaheuristic Algorithm-Based Vibration Response Model for a Gas Microturbine

Montoya-Santiyanes

Rodríguez-Abreo

Rodriguez

et al. 2022

Sensors

View full text Add to dashboard Cite

Data acquisition and processing are areas of research in fault diagnosis in rotating machinery, where the rotor is a fundamental component that benefits from dynamic analysis. Several intelligent algorithms have been used to optimize investigations of this nature. However, the Jaya algorithm has only been applied in a few instances. In this study, measurements of the amplitude of vibration in the radial direction in a gas microturbine were analyzed using different rotational frequency and temperature levels. A response surface model was generated using a polynomial tuned by the Jaya metaheuristic algorithm applied to the averages of the measurements, and another on the whole sample, to determine the optimal operating conditions and the effects that temperature produces on vibrations. Several tests with different orders of the polynomial were carried out. The fifth-order polynomial performed better in terms of MSE. The response surfaces were presented fitting the measured points. The roots of the MSE, as a percentage, for the 8-point and 80-point fittings were 3.12% and 10.69%, respectively. The best operating conditions were found at low and high rotational frequencies and at a temperature of 300 ∘C. High temperature conditions produced more variability in the measurements and caused the minimum value of the vibration amplitude to change in terms of rotational frequency. Where it is feasible to undertake experiments with minimal variations, the model that uses only the averages can be used. Future work will examine the use of different error functions which cannot be conveniently implemented in a common second-order model. The proposed method does not require in-depth mathematical analysis or high computational capabilities.

show abstract

“…As these are the principal motives for fatigue, wear, or damages and catastrophic failure of the system, they need to be considered during dynamic modeling of the rotor-bearing system. 148,183–200…”

Section: Remarkable Findings and Future Directionsmentioning

confidence: 99%

An overview of dynamic modeling of rolling-element bearings

Kumbhar

Sudhagar

Desavale

2020

Noise & Vibration Worldwide

Self Cite

View full text Add to dashboard Cite

The marvelous uniqueness of vibration responses of faulty roller bearings can be simply observed through its vibration signature. Therefore, vibration analysis has been claimed as an effective tool not only for primitive detection but also for subsequent analysis. The dynamic behavior of roller bearings has been investigated by systematic modeling of system and its validation under diverse operating conditions. This article presents an overview of imperative marks in the development of dynamic modeling of rolling-element bearing, which especially predicted vibration responses of damaged bearings. This study aims to address dimensional analysis; a new and imperative way to model the dynamic behavior of rolling-element bearings and their real-time performance in a rotor-bearing system. The findings are described with influential advantages over earlier research to pinpoint the intention behind its development. A literature summary is trailed by remarkable findings and future directions for research.

show abstract

Theoretical and experimental studies to predict vibration responses of defects in spherical roller bearings using dimension theory

Cited by 28 publications

References 27 publications

Non-Linear Regression Models with Vibration Amplitude Optimization Algorithms in a Microturbine

Non-Linear Regression Models with Vibration Amplitude Optimization Algorithms in a Microturbine

Metaheuristic Algorithm-Based Vibration Response Model for a Gas Microturbine

An overview of dynamic modeling of rolling-element bearings

Contact Info

Product

Resources

About