Florian Michael Becker-Dombrowsky

Gear condition monitoring is predominantly executed through the utilization of acceleration sensors positioned on the housing. However, recent advancements have identified measuring the instantaneous angular speed as a compelling alternative as it shortens the transmission path and therefore provides high-quality rotational angle information that can be used to increase damage prediction accuracy, particularly under transient operating conditions. Additionally, there are a variety of methodologies for integrating sensors into gears, which underscores the necessity for high-quality condition data. However, it should be noted that a significant amount of effort is required to successfully integrate these sensors into the rotating system. This publication uses a gear wheel sensor that employs the gear itself as a material measure to acquire rotational angle data and to deduce the damage condition. A magnetoresistive sensor is integrated into the gearbox housing radially facing a ferromagnetic gear and measures the rotational angle by the gear teeth. Various artificial tooth flank damages are applied to the pinion. The rotational angle is measured with the gear sensor, and the damage state is classified with a random forest classifier using established evaluations in the time and frequency domains. The tests are conducted under stationary operating conditions at an array of speed and torque levels. Additionally, they are performed under transient operating conditions, employing speed ramps at constant torque. The results of the classification are evaluated by means of classification accuracy and confusion matrices and compared with those obtained via a classic encoder at the pinion shaft and an acceleration sensor at the gearbox housing.

In the context of advancing digitalization, the focus is on data acquisition using suitable sensors. Therefore, data acquisition and the required sensor technology should be located within the system to measure as close to the process as possible. With rotating systems in mechanical engineering, the integration of sensors on rotating elements poses a challenge, because the recorded data must be transferred from the rotating element to the evaluation unit. One option for this is electrical contacts like slip rings. However, these have the disadvantage that additional elements and installation space are required in the system. In this article, the technical possibilities of using conventional rolling bearings as signal transmission elements are investigated. For this purpose, analog and digital signals are transmitted via radial and axial rolling bearings under different operating conditions. The transmission quality is quantitatively analyzed. Analog voltage signals and digital signals modulated in various ways are used as test signals. It can be seen that the capacitive behavior of the rolling bearings causes a damping signal transmission behavior for analog signals. Axial and radial rolling bearings differ in their resulting damping of the transmitted electrical signals. The carrier frequency of the signals, the oil temperature and the bearing loads have a significant influence on the damping behavior. Robust signal transmission is observed with digital signals. Frequency shift keying and phase shift keying modulations exhibit the lowest transmission errors. Amplitude shift keying can only be used with restrictions due to the pronounced damping behavior of the rolling bearings. Overall, there is a pronounced potential for the transmission of analog and, above all, digital sensor signals via rolling bearings as a transmission element between rotating and stationary systems. In addition to the potential, the work also shows new research directions for further investigations.

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.

Condition monitoring of machine elements becomes more important over the last years. Different approaches to detect failures in mechanical components have been developed. All these methods 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 condition monitoring enables in situ measurement with reduced uncertainties and higher data quality. In the last years, research considering this topic was 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 condition monitoring approaches exists and which 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.

In recent years, electrical damage phenomena in rolling and plain bearings have become increasingly important due to the growing electrification of individual mobility and renewable energy technologies

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.

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.

Failures of machine elements can cause critical machine breakdowns, e.g. bearing damage accounts for almost 20% of all machine failures. Hence, monitoring machine elements is of particular importance for condition monitoring of gearboxes. Therefore, new approaches based on electrical impedance analysis are proposed in literature, e.g. for the measurement of rolling bearing load or damage condition with so-called sensory utilizable machine elements (SuME). For this approach, an electrical signal is transmitted through the structure to be observed and the resulting impedance is measured. With this technique it is possible to detect loads and damages not only for bearings, but also for the entire gearbox. Another application of this method can be the transmission of electrical signals through machine elements and the gearbox themselves. However, uncertainty arises with regard to the distinct signal transmission in these processes, mainly due to parasitic capacitances. In this contribution, the disturbing factors on the measured impedance of a gearbox at different working points are investigated. For this purpose, disturbance factors are systematically identified and used as basis for a full factorial experimental design for screening the operational characteristics of a gearbox. The data obtained from the tests is analyzed to identify correlations between the disturbance factors and parasitic capacitances. The experiments show a speed- and torque-dependent behavior of the parasitic capacitances in a gearbox. These results can be used for further research of SuME and structure-integrated energy supply and signal paths as well as the evaluation of related measurement data by reducing the associated uncertainty.

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.

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.