Data-driven models for predicting remaining useful life of high-speed shaft bearings in wind turbines using vibration signal analysis and sparrow search algorithm

Simple item page
dc.contributor.authorPandit, Ravi
dc.contributor.authorXie, Weixun
dc.date.accessioned2023-10-27T11:45:41Z
dc.date.available2023-10-27T11:45:41Z
dc.date.issued2023-10-16
dc.description.abstractWind turbine bearings play a crucial role in ensuring the safe and efficient operation of wind turbines. Accurate estimation of the remaining useful life (RUL) of bearings can significantly reduce operating and maintenance costs. In this paper, we propose three advanced data-driven models to predict the RUL of high-speed shaft bearings in wind turbines. These models combine the sparrow search algorithm (SSA) with three different regression models, namely support vector machine, random forest (RF) regression and Gaussian process regression. The models are based on features extracted from the vibration signal analysis, and the features are selected based on their monotonicity to evaluate bearing degradation. To optimize the performance of the regression models, all model parameters are tuned using the SSA algorithm. The proposed models are validated using vibration data collected from a real 2 MW commercial wind turbine. Our results demonstrate that the proposed models are effective in predicting the RUL of wind turbine bearings, and the SSA algorithm improves the accuracy of the predictions.en_UK
dc.identifier.citationPandit R, Xie W. (2023) Data-driven models for predicting remaining useful life of high-speed shaft bearings in wind turbines using vibration signal analysis and sparrow search algorithm, Energy Science & Engineering, Volume 11, Issue 12, December 2023, pp. 4557-4569en_UK
dc.identifier.issn2050-0505
dc.identifier.urihttps://doi.org/10.1002/ese3.1597
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/20466
dc.language.isoenen_UK
dc.publisherWileyen_UK
dc.rightsAttribution 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectGaussian process regressionen_UK
dc.subjectrandom forest regressionen_UK
dc.subjectremaining useful life estimationen_UK
dc.subjectsparrow search algorithmen_UK
dc.subjectsupport vector machineen_UK
dc.titleData-driven models for predicting remaining useful life of high-speed shaft bearings in wind turbines using vibration signal analysis and sparrow search algorithmen_UK
dc.typeArticleen_UK

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
Predicting_remaining_useful_life_of_high‐speed_shaft-2023.pdf
Size:
1.76 MB
Format:
Adobe Portable Document Format
Description:
Download
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
1.63 KB
Format:
Item-specific license agreed upon to submission
Description:
Download

Collections

Staff publications (SATM)