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Introduction: The demand to increase reliability and reduce maintenance costs drives the search for new condition monitoring solutions of machines. Bearings are of special interest in this matter as they are main contributors to machine downtime. Therefore, this article investigates the electric behavior of a hydrodynamic journal bearing using impedance measurement. Methods: For that purpose measurements are taken in boundary lubrication, mixed lubrication and hydrodynamic lubrication of a hydrodynamic journal bearing. In order to interpret the behaviour observed in these measurements, a phenomenological model is developed using the quantitative working space model. The measurement results are also interpreted using analogies to the Stribeck curve as a known tribological model. Additionally, measurements in mixed lubrication are analysed in detail. Results: These measurements show that the electric behaviour of journal bearings changes significantly within the mixed lubrication regime. This change in behaviour shows three distinct states within mixed lubrication which helps to identify destructive operating conditions. Furthermore, the measurements potentially allow the detection of deformation or damage of the bearing in mixed lubrication. Discussion: The quantitative analysis of the electric behaviour of journal bearings is possible in the mixed lubrication regime and can be used in future to extract condition and operating information from a system during operation. [ABSTRACT FROM AUTHOR]

Condition monitoring of machine elements becomes more important over the last years. Different approaches to detect failures in mechanical components have been developed. All thesemethods are located at a distance from the point of interest, the observed machine element. This leads to uncertainties in the data, which influences the data quality negatively. Using the electrical impedance for conditionmonitoring enables in situ measurement with reduced uncertainties and higher data quality. In the last years, research considering this topicwas done, but a systematic overview is missing. In this article, a systematic literature research according to the PRISMA approach is fulfilled. The main questions are, what application fields for electrical impedance-based conditionmonitoring approaches exists andwhich research gaps are not addressed yet. At the end, 21 articles are categorized in their application fields. Analyzing their content, research questions are identified which have to be addressed in further investigations. [ABSTRACT FROM AUTHOR]

The opportunities of impedance-based condition monitoring for rolling bearings have been shown earlier by the authors: Changes in the impedance signal and the derived features enable the detection of pitting damages. Localizing and measuring the pitting length in the raceway direction is possible. Furthermore, the changes in features behavior are physically explainable. These investigations were focused on a single bearing type and only one load condition. Different bearing types and load angles were not considered yet. Thus, the impedance signals and their features of different bearing types under different load angles are investigated and compared. The signals are generated in fatigue tests on a rolling bearing test rig with conventional integrated vibration analysis based on structural borne sound. The rolling bearing impedance is gauged using an alternating current measurement bridge. Significant changes in the vibration signals mark the end of the fatigue tests. Therefore, comparing the response time of the impedance can be compared to the vibration signal response time. It can be shown that the rolling bearing impedance is an instrument for condition monitoring, independently from the bearing type. In case of pure radial loads, explicit changes in the impedance signal are detectable, which indicate a pitting damage. Under combined loads, the signal changes are detectable as well, but not as significant as under radial load. Damage-indicating signal changes occur later compared to pure radial loads, but nevertheless enable an early detection. Therefore, the rolling bearing impedance is an instrument for pitting damage detection, independently from bearing type and load angle. [ABSTRACT FROM AUTHOR]

Research into sensory utilization of the impedance measurement of rolling bearings has been intensifying for several years. However, publications in this area are mainly limited to theoretical models and experiments under laboratory conditions. In this contribution, the concept of measuring the impedance of rolling bearings for sensory utilization is implemented within a machine tool spindle. Machine tools can be found in almost all manufacturing companies for the production of components, complete products or prototypes and are therefore one of the most important and relevant production systems. The most common cause of failure is component failure, especially the rolling bearings of the spindle. Failure of the rolling bearings is often the result of incorrect operation and/or overloading. A preventative approach is the sensory monitoring of operating variables. In this work, the spindle design is modified to implement the impedance measurement of rolling bearings and then tests are carried out at free-running and under load. The impedance measurement is analyzed in time and frequency domain. Ultimately, an initial suggestion is made for features in the impedance signal that can be related to various operating parameters. [ABSTRACT FROM AUTHOR]

The integration of Digital Twins (DTs) is becoming increasingly important in various industries. This entails the need for a comprehensive and practical IT infrastructure framework. This paper presents a modifiable medium-level architecture that serves as a link between established reference architectures such as RAMI 4.0 and the pragmatic implementation of Digital Twins. The functionalities of an IT infrastructure are considered, and functional hardware and software components for fulfilling these are described. The proposed architecture is suitable for various deployment scenarios, including local, cloud and hybrid cloud–edge configurations. In order to improve the applicability of the medium-level architecture, a step-by-step procedure is also proposed, which helps to transfer the overarching requirements for a Digital Twin into a suitable IT infrastructure. Finally, the results are demonstrated by an exemplary application to a two-stage industrial gearbox. [ABSTRACT FROM AUTHOR]

This article discusses the behavior of lubricated bearings in electric circuits, which has become increasingly important due to the growing electrification of individual mobility and renewable energy technologies. The research work in this field is predominantly published in German, so this special issue aims to provide a comprehensive overview of the topic. The article explores various models and methods for analyzing the electrical impedance behavior of rolling and plain bearings, as well as the potential for using bearings as integrated sensors in industrial processes. The challenges and potential applications of using electrical properties of bearings for damage detection and predictive maintenance are also discussed. The special issue includes several research papers that cover different aspects of this topic, including electrical bearing currents, lubricant film conditions, surface topology, strain measurement, and the factors influencing bearing currents. The authors express their gratitude to the contributors and reviewers for their valuable contributions. [Extracted from the article]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

This paper focuses on the electrochemical joining of additively manufactured components using simulation-based and experimental methods. The study investigates the influence of cover screens on the filling behavior of the joining zone. Experimental methods involving additive manufacturing and electroplating are combined with simulation models to provide a realistic representation of the joining process. The results show a good agreement between the simulated and experimental findings, indicating the applicability of the simulation model. The parameter study reveals that higher cover factors result in a decrease in the excess material ratio, indicating reduced material deposition outside the joining zone. The filling time required to completely fill the joining zone is influenced by both the cover size and the opening angle of the joining zone. The optimal parameter combinations depend on whether the filling time or the excess material volume is to be minimized. Cavity formation within the joining zone was identified as a critical factor affecting the completeness of the filling. The study provides insights into the influence of cover screens on the electrochemical joining process and offers guidance for optimizing the design of the joining zone. [ABSTRACT FROM AUTHOR]

This work presents an investigation on the influence of pitting in EHL rolling contact. The pitting geometry comes as an extension on the architecture for multi-physical numerical calculations of rolling element bearing contacts based on OpenFOAM. The model utilized is built according to the state-of-the-art for fluid–solid interaction and electro-quasi-static fields. In this framework, the contact is reduced to be two dimensional in order to reduce the computing costs needed. The changes in the electric properties, which are caused by pittings, are measured using the electric capacitance of the contact and put in perspective with regard to the EHL contacts geometry. The investigation delivers an evaluation on how surface degradation, in both the pitting width w and pitting depth d of the contact, affects the electric properties. It can be seen that the maximum deviations with different reduced radii for the same pitting structures are around 5% and would therefore hardly be distinguishable with corresponding measurements. By comparing the same data for the same ratio of pitting width to reduced radius, on the other hand, differences of up to 50% can be seen. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Condition monitoring of technical systems has increasing importance for the reduction of downtimes based on unplanned breakdowns. Rolling bearings are a central component of machines because they often support energy-transmitting elements like shafts and spur gears. Bearing damages lead to a high number of machine breakdowns; thus, observing these has the potential to reduce unplanned downtimes. The observation of bearings is challenging since their behavior in operation cannot be investigated directly. A common solution for this task is the measurement of vibration or component temperature, which is able to show an already occurred bearing damage. Measuring the electrical bearing impedance in situ has the ability to gather information about bearing revolution speed and bearing loads. Additionally, measuring the impedance allows for the detection and localization of damages in the bearing, as early research has shown. In this paper, the impedance signal of five fatigue tests is investigated using individual feature selection. Additionally, the feature behavior is analyzed and explained. It is shown that the three different bearing operational time phases can be distinguished via the analysis of impedance signal features. Furthermore, some of the features show a significant change in behavior prior to the occurrence of initial damages before the vibration signals of the test rig vary from a normal state. [ABSTRACT FROM AUTHOR]

Digital Twins are being used more and more frequently and provide information from the Real Twin for different applications. Measurements on the Real Twin are required to obtain information, which in many cases requires the installation of supplementary sensors. For their conception and design, it is particularly important that the measuring principles are selected purposefully and the appropriate sensors are integrated at the goal-oriented measuring positions without impairing the functions and other properties of the Real Twin by the integration of these sensors. In this article, a "Design for Digital Twin" approach is discussed for the systematic procedure and demonstrated using a multi-staged gearbox as a concrete example. The approach focuses on the mechanical and hardware side of the Real Twin. For the systematic conception and design of the Digital Twin solution, an understanding of the stakeholder demands and the expected use cases is necessary. Based on the stakeholder demands and use cases, the relevant product properties can be determined. Using the relevant properties, an iterative process of conception, design, and analysis takes place. The conception is carried out by means of target-oriented cause–effect analyses, taking into account systemic interrelations of the Real Twin components and systematics for the selection of measurement principles. Systemic considerations, combined with an effect graph, allow for the analysis and evaluation of disturbing factors. [ABSTRACT FROM AUTHOR]

Caused by the progressing digitalization in mechanical engineering, a significant demand for information about characteristic process and state variables of technical systems arises. However, since it is oftentimes neither obvious what nor how to measure, the integration of measuring functions, in particular in terms of a retrofit, represents a current challenge in mechanical engineering. In order to overcome this challenge, an approach for the systematic identification of potential measurands is provided in this contribution. For this purpose, the approach of physical effect catalogs is taken up and used for the systematic identification of potential measurands, starting from a physical variable to be determined. Existing catalog systems have two major limitations with respect to the intended identification of cause-effect relationships: They assume an effect to be realized and a consideration of design parameters of a technical system is not intended. These limitations are overcome by linking the fundamental idea of existing catalog systems with the basics of multipole-based modeling. In this way, a multipole-based effect catalog system is developed. It creates the foundation to systematically include the changes and transformations of a process or state variable to be determined into the identification of potential measurands. [ABSTRACT FROM AUTHOR]

In the course of the electrification of powertrains, rolling element bearings are increasingly subject to electrical damage. In contrast to mechanically generated pittings, voltage-induced surface damage is a continuous process. Though several approaches for the description of the damage state of a bearing are known, a generally accepted quantification for the bearing damage has not been established yet. This paper investigates surface properties, which can be used as a metric damage scale for the quantification of the electric bearing damage progression. For this purpose, the requirements for suitable surface properties are defined. Afterwards, thrust ball bearings are installed on a test rig, with constantly loaded mechanically and periodically damaged electrically in multiple phases. After each phase, the bearings are disassembled, the bearing surfaces are graded and measured for 45 different standardized surface properties. These properties are evaluated with the defined requirements. For the ones meeting the requirements, critical levels are presented, which allow for a quantified distinction between grey frosting and corrugation surfaces. These values are compared with measurements presented in the literature showing that the identified surface properties are suitable for the quantification of electrical bearing damages. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Motivated by the application of industry 4.0 and Internet of Things (IoT) technologies, the development of cyber physical systems (CPS) is gaining momentum. As CPS require multiple measurement technologies to drive the intended function, e.g., condition monitoring and in situ measurement, the integration of measurement systems into industrial processes or individual products becomes a critical activity within the development process. Development methods like the V-Model support developers with methodological guidelines, but the related methods and models do not provide sufficient information regarding the energy supply of embedded systems. If the measurement system, consisting of sensor, calculation and communication unit is integrated inside an either sealed or moveable system, e.g., in a gearbox, ensuring a reliable communication and energy supply is a challenging task. This contribution therefore focuses on the energy supply, in particular the electric power consumption of autarchic measurement systems, referred to as sensor nodes. Based on a literature review of existing physical principles determining the energy consumption of semiconductors, a simple estimation model is derived. Estimation models in the current literature mainly focus on the effects of source code or software in general without analyzing a possible impact of operation strategies, such as generic data processing logics in practical applications. The model presented in this contribution is therefore used to identify the energy consumption of sensor nodes influenced by ambient and operating conditions of sensor nodes. Strategies are examined experimentally using an exemplary sensor node, a climatic chamber and a sensor-integrated gearbox as the system to be observed. An analysis of the conducted experiments leads to a more precise model, which is evaluated regarding its significance for predicting the energy consumption and the underlying simplifications. Finally, general relations influencing the energy consumption are presented and necessary research suggested. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Copyright of Tribologie und Schmierungstechnik is the property of Narr Francke Attempto Verlag GmbH & Co.KG and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Sensing machine elements offer the potential to upgrade conventional machine elements by extending their primary function to be able to measure a quantity of interest at its point of origin in a technical system, the so-called in situ measurement. To ensure the functionality of these next generation machine elements, special attention must be paid to uncertainty in terms of modelling to be able to correctly evaluate the provided signal and obtain reliable information. Consequently, this contribution describes an approach to classify uncertainty in sensing technology, especially in SMEs, based on the amount of available information, which can be used as a point of departure to reduce the impact of occurring uncertainty to improve the robustness of the obtained signal. Starting from the understanding of uncertainty and the corresponding classification scheme as well as its linkage to robust design from the Collaborative Research Centre 805, a quantitative model is presented to determine the impact of uncertainty on a measuring signal. The applicability of the proposed approach is demonstrated using the example of a sensing timing belt by taking into account the uncertainty from the SME itself and also from the surrounding technical system. [ABSTRACT FROM AUTHOR]

A mandatory requirement for the concept of intelligent systems and of digital or cyber-physical twins is the availability of high-quality data. Therefore, the authors investigate the possibility to integrate sensors, actuators and information technologies in standardized machine elements such as screws, bearings and couplings. In this paper, the focus is on sensing machine elements, which are a sub-category of mechatronic machine elements. To gain insights about those in development as well as to verify and validate their functionality, prototypes are needed. Those prototypes should help the designer to gain knowledge about the product in development and they should preferably be developed with low efforts. Therefore, a method is proposed to analyse concepts of mechatronic machine elements, especially sensing machine elements, regarding critical aspects that may interfere with the functionality of the product. The method is based on analysing the flow of the signal that is used for the measurement, starting from its mechanical origin and ending at the analysis unit. Different examples of sensing machine elements are given in this article and the respective flow of the usable signal is analysed, leading to the identification of subsystems that can be tested individually. Based on this, prototypes for the subsystems are developed and introduced. [ABSTRACT FROM AUTHOR]

Copyright of Tribologie und Schmierungstechnik is the property of Narr Francke Attempto Verlag GmbH & Co.KG and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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. [ABSTRACT FROM AUTHOR]