Rolling bearings are frequently subjected to high stresses within modern machines. To prevent bearing failures, the topics of condition monitoring and predictive maintenance have become increasingly relevant. In order to efficiently and reliably maintain rolling bearings in a predictive manner, an estimate of the remaining useful life (RUL) is of great interest. The RUL prediction quality achieved when using machine learning depends not only on the selection of the sensor data used for condition monitoring, but also on its preprocessing. In particular, the execution of so-called feature engineering has a major impact on prediction quality. Therefore, in this paper, various methods of feature engineering are presented based on rolling–bearing endurance tests and recorded structure-borne sound signals. The performance of these methods is evaluated in the context of a regression-based RUL model. Furthermore, the way in which the quality of RUL prediction can be significantly improved is demonstrated, by adding further processed, time-considering features.

The engineering challenge of rolling bearing condition monitoring has led to a large number of method developments over the past few years. Most commonly, vibration measurement data are used for fault diagnosis using machine learning algorithms. In current research, purely data-driven deep learning methods are becoming increasingly popular, aiming for accurate predictions of bearing faults without requiring bearing-specific domain knowledge. Opposing this trend in popularity, the present paper takes a more traditional approach, incorporating domain knowledge by evaluating a variety of feature engineering methods in combination with a random forest classifier. For a comprehensive feature engineering study, a total of 42 mathematical feature formulas are combined with the preprocessing methods of envelope analysis, empirical mode decomposition, wavelet transforms, and frequency band separations. While each single processing method and feature formula is known from the literature, the presented paper contributes to the body of knowledge by investigating novel series connections of processing methods and feature formulas. Using the CWRU bearing fault data for performance evaluation, feature calculation based on the processing method of frequency band separation leads to particularly high prediction accuracies, while at the same time being very efficient in terms of low computational effort. Additionally, in comparison with deep learning approaches, the proposed feature engineering method provides excellent accuracies and enables explainability.

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 rotating machinery, rolling bearings are often the components limiting service life. To avoid unforeseen downtimes, they have to be maintained. For reasons of safety and cost optimization, condition-based maintenance is increasingly being used. Knowing the condition of the components that are critical to wear is essential for this maintenance approach. The insight about the condition is achieved by means of suitable measurement variables, which can be used to automatically detect the condition of the components using machine learning. The quality of the condition monitoring is strongly dependent on the available measurement data and its preprocessing. For condition monitoring of rolling bearings, structure-borne sound signals can be used. The decisive factor here is to determine so-called features from the high-frequency sampled structure-borne sound signals. These features are supposed to reflect the characteristic properties of the measured signals. At the same time, the amount of data is considerably reduced. In this article, different methods of feature engineering based on structure-borne sound are investigated. For this purpose, the wear of rolling bearings is considered in the context of endurance tests. A new feature generation method is presented and compared to common methods from literature.

Locating bearing track rollers are used, for example, in monorail transport systems to enable relative movement between the rail and the shuttle. Due to the two-point contact, both radial and axial forces can be transmitted simultaneously. Since friction is involved, the state of the art does not provide any calculation rules for the dimensioning and design. The development of a calculation model with sophisticated commercial software brings its difficulties since no plausibility check is possible using existing models. For this reason, a model based on analytical descriptions including the Hertzian and the elastic half space theories is presented in this paper. It bridges the gap between very simple approaches and widely developed commercial software. With this model, the contact forces, friction forces, surface tensions, relative velocities and subsurface stresses can be calculated for both free and driven rolling. The main advantages are that the model is easy to apply, and thus comparisons between different track roller designs can be made quickly.