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.

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.

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.