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.

In this paper the development process of a sensing rolling bearing is presented, from which finally design rules for sensing machine elements are derived. In the first step, the requirements of the users are determined. It turns out user of sensing machine elements want to continue to use the advantage of the standardized machine elements and costs should not be incurred by redesign or complex assembly. With these requirements the development of the sensing rolling bearing is started, in which the different presented technologies are reviewed for their suitability regarding the requirements. With the selected technology measuring the electric rolling bearing impedance to estimate rolling bearing loads, a first prototype is developed by creating a functional structure of the product and focusing on the partial solution of the most relevant partial functions. This prototype is then tested with regard to its functionality. Finally, generalizable design rules for sensing machine elements are derived from the development.

The ongoing digitalization of processes and products in mechanical engineering is accompanied by an increasing demand for data. In order to provide this data, technical systems are being extended with sensory functions. To supply those sensory functions on rotating elements—such as shafts—with electrical energy, and to be able to transmit signals out of the system, sliding contacts can be used as a cost-effective and established solution. However, if electrical properties of machine elements are utilized for sensing purposes, such as condition monitoring of rolling element bearings by means of impedance measurement, sliding contacts are directly in the measurement path and can thus influence the measured impedance. The aim of this paper is to analyze the impedance of graphite and silver graphite carbon brushes under different rotational speeds, in different positions, and with different carrier frequencies. The material of the carbon brushes as well as the position have significant effects on the impedance behavior. Furthermore, carbon brushes show a significant running-in behavior. The results are discussed, and indications for use in impedance measurements are given. Silver graphite carbon brushes in axial positioning are particularly suitable for impedance measurements of sensory utilizable machine elements. Sufficient running-in time must be considered.

Various methods for calculating the capacitance of unloaded rolling elements are compared to improve the electric characterization of rolling element bearings. Semi-analytical approximations and finite element simulations are applied and a closed-form analytical two-dimensional solution is derived for comparison. The results show that the most common semi-analytical approximation, which uses effective radii to express the contact geometry, reproduces the trend from numerical and analytical calculations, but overestimates the results derived from numerical simulations. Another semi-analytical approach is presented that delivers better results for the capacitance of unloaded point contacts.