Predicting the Electrical Impedance of Rolling Bearings Using Machine Learning Methods

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.

A Review on Sensor‐Integrating Machine Elements

“…Capacitive effects are the basis of the sensory concept for rolling element bearings, in which the isolating lubrication film thickness depends on temperature, relative speed, and bearing load and can be used to calculate the current load and speed as described by Martin et al, [22] Schirra et al [23,24] and Kirchner et al [48] Similarly, the capacitive effect can also be detected in hydrodynamic journal bearings, [2,49] and even in dry lubricated sliding bearings. [50] The idea to use the electric property of the (hydrodynamic) lubrication film as such was used even earlier to estimate the lubrication film thickness as an application in machine elements.…”

“…[ 23,24 ] and Kirchner et al. [ 48 ] Similarly, the capacitive effect can also be detected in hydrodynamic journal bearings, [ 2,49 ] and even in dry lubricated sliding bearings. [ 50 ] The idea to use the electric property of the (hydrodynamic) lubrication film as such was used even earlier to estimate the lubrication film thickness as an application in machine elements.…”

Electrical Impedance Spectroscopy for Precise Film Thickness Assessment in Line Contacts

“…As mentioned previously, the test bearing in this work concerns a purely axially loaded cylindrical roller thrust bearing, in which all contacts are assumed to be identical and representable by the same electrical model of Equation ( 4), as illustrated in Figure 4. The lubrication state for the EHL contacts in this bearing is typically defined by means of the following three non-dimensional parameters, denoting the dimensionless speed, the dimensionless load, and the lubricant parameter, respectively [47,48] (Figure 1).…”

Classification between Normal and Cancerous Human Urothelial Cells by Using Micro-Dimensional Electrochemical Impedance Spectroscopy Combined with Machine Learning

“…Resistance and reactance are not constant, but depend on frequencies, and therefore, these signals provide a spectrum containing rich information about materials. Recently, machine learning (ML) has been applied to such impedance spectra to use them as fingerprints of materials for estimating target samples; for example, lithium-ion battery lifetime [ 9 ], metal corrosion [ 10 ], wood moisture content [ 11 ], milk adulteration [ 12 ], and health state of rolling bearing [ 13 ]. Furthermore, in the biomedical field, breast tissue [ 14 , 15 ] and prostate tissue [ 16 ] have been analyzed using combinations of impedance spectra and ML to detect subtle spectral patterns, and differentiate between normal and pathological tissues.…”