Rolling bearings face different damaging effects: Besides mechanical effects, current-induced bearing damage occurs in electrical drive systems. Therefore, it is of increasing interest to understand the differences leading to known electrical damage patterns. It is of utmost importance not to consider the harmful current passage in the machine element as an isolated phenomenon but to take into account the whole drive system consisting of the machine elements, the electric motor and the connected power electronics. This publication works toward providing an overview of the state-of-the-art of research regarding electrical bearing currents.

In order to fully exploit the potential of the rapidly progressing digitalisation of technical systems, it is necessary to provide reliable and significant process and condition related data. In this context, solutions are especially aspired to allow a simple integration into the surrounding system and to influence it as little as possible. The main challenges regarding the measurement of process and condition data in the operation and control of technical systems as well as in test environments are identified and presented at the beginning of this article. A promising approach to meet the resulting requirements is the integration of sensory functions into simple standardised machine elements. In order to facilitate the discussion and interdisciplinary development of machine elements with sensory functions, an extension of the existing classification of mechatronic machine elements is introduced and illustrated with examples. The introduced classification takes into account the classification according to Stücheli and Meboldt and is based on a comparison of conventional and mechatronic machine elements on a functional level with regard to the function structure. As a result, the three classessensor carrying machine elements,sensor integrating machine elementsandsensory utilizable machine elementsare introduced and subsequently discussed in more detail on the basis of examples. Finally, an outlook is given on the main research areas with regard to the development of mechatronic machine elements. Key aspects include working principles and effects for application in mechatronic machine elements, system analysis with regard to load conditions, power supply of sensor and data processor in the environment of the machine element as well as data processing and signal transmission under typical environmental conditions of mechanical engineering.

Digital Twins offer vast potential, yet many companies, particularly small and medium-sized enterprises, hesitate to implement them. This hesitation stems partly from the challenges posed by the interdisciplinary nature of creating Digital Twins. To address these challenges, this paper explores systematic approaches for the development and creation of Digital Twins, drawing on relevant methods and approaches presented in the literature. Conducting a systematic literature review, we delve into the development of Digital Twins while also considering analogous concepts, such as Cyber-Physical Systems and Product-Service Systems. The compiled literature is categorised into three main sections: holistic approaches, architecture, and models. Each category encompasses various subcategories, all of which are detailed in this paper. Through this comprehensive review, we discuss the findings and identify research gaps, shedding light on the current state of knowledge in the field of Digital Twin development. This paper aims to provide valuable insights for practitioners and researchers alike, guiding them in navigating the complexities associated with the implementation of Digital Twins.

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 modern machine architecture, complex mechanical components guarantee the function of the system. Specific maintenance rates reduce maintenance effort and cost. The combination of load and failure monitoring provide data to establish an individual maintenance rate depending on component specific remaining lifetime. In addition, algorithms can optimise the operation strategy to relieve the weakest components and extend the system lifetime. Adapted ball bearings contain a sensor mechanism depending on the relation of beating load and electric bearing impedance to measure load and failure data.