Expansion of the Metrological Visualization Capability by the Implementation of Acoustic Emission Analysis

Abstract

The acquisition and evaluation of acoustic emissions (AE) in tribology have proven to be a meaningful tool for condition monitoring and offer possibilities to deepen the understanding of tribological processes. The authors used this technology with the aim of expanding existing test methodologies towards increased visualization capability of tribological processes and investigated the correlation between tribological processes and acoustic emissions on a Ring-on-Disc and a close-to-component journal bearing test setting. The results of this study include the description of friction as well as wear processes and prove the usability of several AE evaluation parameters whereby a close correlation between AE and tribological processes can be shown. Consequently, it was possible to expand the visualization and evaluation capabilities of the test settings offering additional insights by making use of AE.

Evaluation of Wear Phenomena of Journal Bearings by Close to Component Testing and Application of a Numerical Wear Assessment

“…The tests were conducted by the use of a Bearing Segment Test Adapter (BSTA) in combination with a rotary tribometer TE92 from Phoenix Tribology (Basingstoke, UK). This setting has been a reliable testing facility throughout recent years allowing detailed investigations of friction and wear processes of a simplified journal bearing contact while granting a close link to industrial applications [5,24,25]. The investigated journal bearing specimens depict a multilayer structure of which the wear process of the top sliding layer, which consists of a PbSn based material is of interest.…”

A Novel Approach for Modeling Surface Effects in Hydrodynamic Lubrication

“…In addition to the experimental investigation [1][2][3][4][5], the simulation of lubricated contacts is an important tool for the design process of tribological systems for different applications [6][7][8][9][10]. For the calculation of the hydrodynamic lubrication, the Reynolds equation is commonly used [11].…”

Lubrication Regime Classification of Hydrodynamic Journal Bearings by Machine Learning Using Torque Data

“…Naturally, these sensors deliver time series data, which is usually processed to statistical scalar values, such as mean value, standard deviation, root mean square (RMS), or skewness. The magnitude of these values can be used in order to detect characteristics of the tribosystems [15]. Another approach is to perform angle synchronous averaging (ASA), which delivers values for a certain position range of the rotating machinery component [16,17].…”

“…However, in the area of journal bearings it is less clear how different contact, lubrication, or wear scenarios influence the frequency range of measured data. Nevertheless, some authors already performed studies regarding the tribological state of journal bearings [15,[19][20][21].…”