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On the Importance of Temporal Information for Remaining Useful Life Prediction of Rolling Bearings Using a Random Forest Regressor

Bienefeld, Christoph ; Kirchner, Eckhard ; Vogt, Andreas ; Kacmar, Marian (2022)
On the Importance of Temporal Information for Remaining Useful Life Prediction of Rolling Bearings Using a Random Forest Regressor.
In: Lubricants, 2022, 10 (4)
doi: 10.26083/tuprints-00021279
Article, Secondary publication, Publisher's Version

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Item Type: Article
Type of entry: Secondary publication
Title: On the Importance of Temporal Information for Remaining Useful Life Prediction of Rolling Bearings Using a Random Forest Regressor
Language: English
Date: 6 May 2022
Place of Publication: Darmstadt
Year of primary publication: 2022
Publisher: MDPI
Journal or Publication Title: Lubricants
Volume of the journal: 10
Issue Number: 4
Collation: 12 Seiten
DOI: 10.26083/tuprints-00021279
Corresponding Links:
Origin: Secondary publication DeepGreen
Abstract:

Rolling bearings are frequently subjected to high stresses within modern machines. To prevent bearing failures, the topics of condition monitoring and predictive maintenance have become increasingly relevant. In order to efficiently and reliably maintain rolling bearings in a predictive manner, an estimate of the remaining useful life (RUL) is of great interest. The RUL prediction quality achieved when using machine learning depends not only on the selection of the sensor data used for condition monitoring, but also on its preprocessing. In particular, the execution of so-called feature engineering has a major impact on prediction quality. Therefore, in this paper, various methods of feature engineering are presented based on rolling–bearing endurance tests and recorded structure-borne sound signals. The performance of these methods is evaluated in the context of a regression-based RUL model. Furthermore, the way in which the quality of RUL prediction can be significantly improved is demonstrated, by adding further processed, time-considering features.

Uncontrolled Keywords: rolling bearings, remaining useful life, machine learning, feature engineering, condition monitoring, structure-borne sound, random forest, regression
Status: Publisher's Version
URN: urn:nbn:de:tuda-tuprints-212793
Classification DDC: 600 Technology, medicine, applied sciences > 620 Engineering and machine engineering
Divisions: 16 Department of Mechanical Engineering > Institute for Product Development and Machine Elements (pmd)
Date Deposited: 06 May 2022 11:20
Last Modified: 14 Nov 2023 19:04
SWORD Depositor: Deep Green
URI: https://tuprints.ulb.tu-darmstadt.de/id/eprint/21279
PPN: 499801814
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