Abstract
Tribologists often rely on triboexperiments to investigate factors that affect a tribosystem. The inherent dynamic behavior of the respective tribometer setups and its effect on data interpretation remain often unknown. In this study, a comprehensive analysis of sensor data obtained from lubricated and dry triboexperiments is performed. Data are generated on a pin-on-disc test rig with a silicon nitride ball on a steel disc contact with a rotation frequency of ~3 Hz. High-speed acquisition of sensor data up to 5 kHz is performed to resolve changes in the data within individual cycles. The characteristic frequencies of the system and their temporal evolution are determined via time-frequency analysis, which reveals periodic patterns in the sensor data. Cycle-based data evaluation allows the detection of localized events and changes during an operation and considerably reduces the apparent measurement uncertainty, as compared with an unreduced dataset. The data analysis and visualization routines presented herein may serve as a prototype for further application to tribometer setups.
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Abbreviations
- μ :
-
Coefficient of friction
- A :
-
Cross-sectional area of the wear profile (mm2)
- d max :
-
Maximum wear depth (mm)
- F f :
-
Friction force (N)
- F N :
-
Normal force (N)
- h ball :
-
Wear height of ball (mm)
- R :
-
Radius of ball (mm)
- r :
-
Radius of apparent contact surface (mm)
- r track :
-
Nominal track radius (mm)
- S xx(f,τ):
-
Spectral energy density of force sensor signal (N2·s2)
- V ball :
-
Wear volume of ball (mm3)
- V disc :
-
Wear volume on disc surface (mm3)
- w(t−τ):
-
Windowing function
- X(f,τ):
-
STFT of a force sensor signal (N·s)
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Acknowledgements
This work was funded by the Austrian COMET Program (Project InTribology, No. 872176) via the Austrian Research Promotion Agency (FFG) and the Provinces of Niederösterreich and Vorarlberg, and has been carried out within the Austrian Excellence Centre of Tribology (AC2T research GmbH).
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Josef PROST. He received his Ph.D. degree in physics from Vienna University of Technology, Austria, in 2018. He has been working as a scientist at the Austrian Excellence Centre for Tribology (AC2T research GmbH) in Wiener Neustadt, Austria, since 2019. His main research interest is the application of advanced data analysis and visualization methods to tribological research questions, including multivariate statistical analysis as well as classification of the operation state and anomaly detection using machine learning models.
Guido BOIDI. He is a scientist at AC2T research GmbH (Austria) since 2019. He received his Ph.D. degree from the University of São Paulo (Brazil) in 2019, where he studied the tribological effect of surface irregularities (laser texturing and porosity in sintered materials) under the guidance of Prof. Izabel Fernanda MACHADO. He was a visitor Ph.D. student for one year at Imperial College London (2018–2019) under the guidance of Prof. Daniele DINI. His research interests involve surface texturing, tribology of powder metallurgy, spark plasma sintering, and sintered bearings and gears.
Thomas LEBERSORGER. He is a scientist at AC2T research GmbH (Austria) since 2004, where he is head of the department “Tribosystem Characterisation”. He received his Dipl.-Ing. (FH) degree in mechatronics and microsystems in 2005 and his M.Sc. degree in tribology and surface engineering in 2013 from the University of Applied Sciences Wiener Neustadt. He has a broad experience in designing customer-orientated procedures for tribological tests. His research interests include rolling contact fatigue in rail/wheel contacts and data analysis.
Markus VARGA. He is currently leading the strategic research area “Synaptic Tribology” at the Austrian Excellence Centre for Tribology (AC2T research GmbH). He received his M.Sc. degree at the University of Applied Science Wiener Neustadt, Austria, in mechatronics and completed his Ph.D. degree in tribology at the Montanuniversität Leoben, Austria. His main research field is the optimization of industrial maintenance by tribological measures for more than 10 years, i.e., wear protection, sensors for early detection of failures.
Georg VORLAUFER. He is currently a principal scientist at AC2T research GmbH, the Austrian Excellence Centre for Tribology. He completed his M.S. degree in physics at Vienna University of Technology, Austria, in 1998 and received his Ph.D. degree in physics in 2002 from the same institution. Between 1998 and 2001, he carried out his Ph.D. studies in the field of vacuum and surface science at CERN (Geneva, Switzerland). He has more than 18 years of experience in the field of tribology. Although since many years his research interests have been mainly in the field of physics-based modelling and simulation of tribological systems, he is currently concentrating on tribology-related aspects of data science, machine learning, and artificial intelligence.
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Prost, J., Boidi, G., Lebersorger, T. et al. Comprehensive review of tribometer dynamics-Cycle-based data analysis and visualization. Friction 10, 772–786 (2022). https://doi.org/10.1007/s40544-021-0534-0
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DOI: https://doi.org/10.1007/s40544-021-0534-0