Shafts in ICEs, such as crankshafts or camshafts, are usually supported by sliding bearings. If lubrication at the bearings is insufficient, metal-to-metal contacts become likely and thus wear and bearing failure can occur. To avoid critical engine operation and to increase engine durability, LEC’s researchers have developed a powerful tool for condition monitoring of sliding bearings in ICEs. They set up a data-driven model for predicting the temperature of the crankshaft main bearings. The model takes parameters from the current engine operating condition, such as load and speed, as input data and outputs the corresponding bearing temperature. In combination with online bearing temperature measurements, such a model can be used to accurately detect anomalies at the bearings in real time during engine operation.

Christian Laubichler, , Matheus Marques da Silva, Andreas Wimmer, and Gunther Hager
Data-Driven Sliding Bearing Temperature Model for Condition Monitoring in Internal Combustion Engines
Journal; DOI; Erscheinungsdatum Lubricants 2022, 10, 103; 22 May 2022


👉Check all our publications:

#LEC #largeengine #bearing #conditionmonitoring #machinelearning #ML #AI #datascience #digital #datadriven #python #scikitlearn #R #rstats #ggplot2