Sensorische Eigenschaften von Wälz- und Gleitlagerungen – Beherrschen von Unsicherheiten von und durch die Zusatzfunktion

Neu, Marcel; Harder, André; Kirchner, Eckhard

doi:10.51202/9783181023488-183

Cited by 5 publications

(5 citation statements)

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“…An additional sensor-integrating condition monitoring option is the measurement of the electrical contact voltage between the shaft and the plain bearing. [258,259] An electric potential is generated between the plain bearing and the shaft for this application. In the case of a hydrodynamic operation, an infinitely high resistance exists between the contact surfaces, as the lubricants used are usually non-conductive.…”

Section: Plain Bearingsmentioning

confidence: 99%

A Review on Sensor‐Integrating Machine Elements

Kirchner,

Wallmersperger,

Gwosch

et al. 2024

Advanced Sensor Research

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This contribution summarizes the current state of research regarding so‐called sensor‐integrating machine elements as an enabler of digitalization in mechanical engineering and——if available—their application in industry. The focus is on the methodical aspects of the development of these machine elements in general as well as specific sensor‐integrating machine elements that are either already in use or currently under development. Developmental aspects include the robust design of initially evaluated concepts for sensor‐integrating machine elements as well as their modularization. Smart materials with sensory functions are included in the analysis as well as the differentiation with regard to add‐on sensors. The aim of the authors interlinked by a special research program funded by the German Research Foundation (DFG) is to facilitate the exchange with other researchers with the help of the comprehensive overview given in this contribution. The contribution concludes with a brief discussion of open challenges, such as the energy supply and data transfer in rotating systems and also data security.

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Section: Plain Bearingsmentioning

confidence: 99%

A Review on Sensor‐Integrating Machine Elements

Kirchner,

Wallmersperger,

Gwosch

et al. 2024

Advanced Sensor Research

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“…Based on the information about the excitation speed contained in the measured impedance, there is the assumption that the measured rotational speed can be substituted by the amplitude spectrum of the impedance [1,2,20]. Investigating this hypothesis from a machine learning perspective can serve as a basis for further research in this area.…”

Section: Model Predictors Selectionmentioning

confidence: 99%

“…Based on the information about the excitation speed contained in the measur pedance, there is the assumption that the measured rotational speed can be substitu the amplitude spectrum of the impedance [1,2,20]. Investigating this hypothesis machine learning perspective can serve as a basis for further research in this a showing that the measured velocity can be replaced with the frequency of the imp signal using machine learning algorithms, it should be possible to predict the bearin without velocity measurement data in the future, since the impedance is measu load prediction anyway [1].…”

Section: Model Predictors Selectionmentioning

confidence: 99%

Predicting the Electrical Impedance of Rolling Bearings Using Machine Learning Methods

et al. 2022

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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.

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“…This method takes advantage of the inherent electrical impedance of rolling bearings. The electrical impedance of rolling and journal bearings has found some applications as a sensor technology in the past years [4][5][6][7][8]. The fact that no additional sensor technology has to be integrated in the machine element facilitates the integration of the sensory function without requiring additional installation space, so that the user does not have to make any changes to the design [9][10][11].…”

Section: Introductionmentioning

confidence: 99%