Predicting the Electrical Impedance of Rolling Bearings Using Machine Learning Methods

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

On the Importance of Temporal Information for Remaining Useful Life Prediction of Rolling Bearings Using a Random Forest Regressor

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

Analyzing Ball Bearing Capacitance using Single Steel Ball Bearings - Data

Supplementary data to the publication "Analyzing Ball Bearing Capacitance using Single Steel Ball Bearings" by Steffen Puchtler, Julius van der Kuip and Eckhard Kirchner published in Tribology Letters by Springer. Capacitance measurements of hybrid ball bearings with a single steel rolling element were carried out. This helps to measure only one current path through the bearing at a time and thus, gives a much clearer picture of the contact capacitance of rolling elements in and out of the load zone. Provided is raw and evaluated measurement data as well as calculation results

Introducing an Open-Source Simulation Model for Track Rollers Considering Friction

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

Adaptivity as a Property to Achieve Resilience of Load-Carrying Systems

Load-carrying systems often suffer from unexpected disruptions which can cause damages or system breakdowns if they were neglected during product development. In this context, unexpected disruptions summarize unpredictable load conditions, external disturbances or failures of system components and can be comprehended as uncertainties caused by nescience. While robust systems can cope with stochastic uncertainties, uncertainties caused by nescience can be controlled only by resilient load-carrying systems. This paper gives an overview of the characteristics of resilience as well as the time-dependent resilient behaviour of subsystems. Based on this, the adaptivity of subsystems is classified and can be distinguished between autonomous and externally induced adaption and the temporal horizon of adaption. The classification of adaptivity is explained using a simple example of a joint brake application

Sensory Utilizable Design Elements: Classifications, Applications and Challenges

The sensory acquisition of in situ data in technical systems is one of the key requirements set by ongoing digitalization. The sensory utilization of mechanical design elements is a step towards the accomplishment of this requirement. To set a common ground for further research in the context of sensory utilizable design elements, this paper reviews the current state of research in this topic. First, the aim, potentials and classification of sensory utilizable design elements are introduced. Next, examples of sensory utilizable design elements are presented. These examples are used to demonstrate the technical and methodical challenges that have to be addressed in order to establish sensory utilizable design elements as a solution for the requirements of digitalization

Investigation of Feature Engineering Methods for Domain-Knowledge-Assisted Bearing Fault Diagnosis

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

Electrical Bearing Damage, A Problem in the Nano- and Macro-Range

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

Systemic Conception of the Data Acquisition of Digital Twin Solutions for Use Case-Oriented Development and Its Application to a Gearbox

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

Electrical Bearing Damage, A Problem in the Nano- and Macro-Range

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