Kirchner, Eckhard ; Bienefeld, Christoph ; Schirra, Tobias ; Moltschanov, Alexander (2022)
Predicting the Electrical Impedance of Rolling Bearings Using Machine Learning Methods.
In: Machines, 2022, 10 (2)
doi: 10.26083/tuprints-00021023
Artikel, Zweitveröffentlichung, Verlagsversion

Kurzbeschreibung (Abstract)

The present paper describes a measurement setup and a related prediction of the electrical impedance of rolling bearings using machine learning algorithms. The impedance of the rolling bearing is expected to be key in determining the state of health of the bearing, which is an essential component in almost all machines. In previous publications, the determination of the impedance of rolling bearings has already been advanced using analytical methods. Despite the improvements in accuracy achieved within the calculations, there are still discrepancies between the calculated and the measured impedance, leading to an approximately constant off-set value. This discrepancy motivates the machine learning approach introduced in this paper. It is shown that with the help of the data-driven methods the difference between analytical prediction and measurement is reduced to the order of up to 2% across the operational range analyzed so far. To introduce the context of the research shown, first the underlying physics of bearing impedance is presented. Subsequently different machine learning approaches are highlighted and compared with each other in terms of their prediction quality in the results part of this paper. As a further aspect, in addition to the prediction of the bearing impedance, it is investigated whether the rotational speed present at the bearing can be predicted from the frequency spectrum of the impedance using order analysis methods which is independent from the force prediction accuracy. The background to this is that, if the prediction quality is sufficiently high, the additional use of speed sensors could be omitted in future investigations.

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