TY  - JOUR
U1  - Zeitschriftenartikel, wissenschaftlich - begutachtet (reviewed)
A1  - Schwendemann, Sebastian
A1  - Amjad, Zubair
A1  - Sikora, Axel
T1  - A survey of machine-learning techniques for condition monitoring and predictive maintenance of bearings in grinding machines
JF  - Computers in Industry
N2  - 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 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.
KW  - Predictive maintenance
KW  - Bearings
KW  - Remaining useful life
KW  - Fault classification
KW  - Grinding machines
KW  - Machine-learning
Y1  - 2021
SN  - 0166-3615
SS  - 0166-3615
U6  - https://doi.org/10.1016/j.compind.2020.103380
DO  - https://doi.org/10.1016/j.compind.2020.103380
VL  - 125
SP  - 1
EP  - 17
PB  - Elsevier Science
ER  -