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A survey of machine-learning techniques for condition monitoring and predictive maintenance of bearings in grinding machines

  • It is important to minimize the unscheduled downtime of machines caused by outages of machine components in highly automated production lines. Considering machine tools such as, grinding machines, the bearing inside of spindles is one of the most critical components. In the last decade, research has increasingly focused on fault detection of bearings. In addition, the rise of machine learningIt is important to minimize the unscheduled downtime of machines caused by outages of machine components in highly automated production lines. Considering machine tools such as, grinding machines, the bearing inside of spindles is one of the most critical components. In the last decade, research has increasingly focused on fault detection of bearings. In addition, the rise of machine learning concepts has also intensified interest in this area. However, up to date, there is no single one-fits-all solution for predictive maintenance of bearings. Most research so far has only looked at individual bearing types at a time. This paper gives an overview of the most important approaches for bearing-fault analysis in grinding machines. There are two main parts of the analysis presented in this paper. The first part presents the classification of bearing faults, which includes the detection of unhealthy conditions, the position of the error (e.g. at the inner or at the outer ring of the bearing) and the severity, which detects the size of the fault. The second part presents the prediction of remaining useful life, which is important for estimating the productive use of a component before a potential failure, optimizing the replacement costs and minimizing downtime.show moreshow less

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Metadaten
Author:Sebastian Schwendemann, Zubair Amjad, Axel SikoraORCiDGND
Publisher:Elsevier Science
Year of Publication:2021
Language:English
Tag:Bearings; Fault classification; Grinding machines; Machine-learning; Predictive maintenance; Remaining useful life
DDC classes:600 Technik, Medizin, angewandte Wissenschaften / 620 Ingenieurwissenschaften und Maschinenbau
Parent Title (English):Computers in Industry
Volume:125
ISSN:0166-3615
First Page:1
Last Page:17
Document Type:Article (reviewed)
Institutes:Bibliografie
Open Access:Zugriffsbeschränkt
Release Date:2022/01/14
Licence (German):License LogoUrheberrechtlich geschützt
DOI:https://doi.org/10.1016/j.compind.2020.103380