In this work, an advanced, numerical simulation method based on finite element analyses was developed in order to simultaneously take into account both roller- and structural-induced ring creeping phenomena. Ring creeping in general refers to a failure mode caused by a (non-bolted) bearing ring rotating relatively to its adjacent component such as, e.g., shaft or housing during operation. In particular, the coefficient of friction at the contact interface between bearing ring and adjacent component has a crucial influence. In order to consider this effect, a bearing ring creeping test rig based on component-like specimen was developed. Experimental results with respect to (i) measured creeping parameters such as creeping distance and (ii) the coefficient of friction due to run-in effects were described. Finally, experimental and numerical results were compared qualitatively to approve the reasonableness of the simulation model. The developed simulation approach enables the consideration of the entire drive train system within the micro-scale creeping evaluation procedure and therefore supports both drive train and bearing design-specific optimization measures in order to increase the reliability and robustness of a main bearing arrangement.
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Data acquisition in (pre)clinical studies is often based on a hypothesis. Numerical algorithms, however, may help to find biomarkers from existing data without formulating any hypothesis. By simply assessing whether a statistical relationship exists between two parameters from a (unlimited) database, every (in)conceivable combination of data becomes a hypothesis. The aim was to create an unbiased and highly automated approach for secondary analysis of (pre)clinical research, including the possibility of a non-linear functional relationship. In our example, an almost homogeneous database was formed by overall 45 parameters (vital, blood and plasma parameters) measured in 11 individual experimental studies at 6 different time points using 57 rats without and 63 rats with systemic inflammation following lipopolysaccharide infusion. For each rat, four group classifiers (treatment, survival, study, ID) were used to get valid samples by a later filtering of the statistical base. Any information about the hypothesis leading to the respective studies was suppressed. In order to assess whether a statistical relationship exists, a total of six different functional prototypes (linear and non-linear) were postulated and examined for their regression. Regression quality, correlation and significance were obtained in form of matrices. In our example, ultimately 510 300 regressions were optimized, automatically evaluated and filtered. The developed algorithm is able to reveal statistical relationships from a nearly crude database with low effort by systematic and unbiased analysis. The finding of well-known correlations proves its reliability, whose validity could be increased by clean aggregation of different studies. In addition, new interesting hints for future research could be gained. Thus, unknown markers can be found which are associated with an increased risk of death during systemic inflammation and sepsis. A further development of the program is planned including multiple regressions (more than two parameters could be related to each other) or cluster analysis.
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