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Article

Data-Driven Sliding Bearing Temperature Model for Condition Monitoring in Internal Combustion Engines

1
Large Engines Competence Center GmbH, 8010 Graz, Austria
2
Institute of Thermodynamics and Sustainable Propulsion Systems, Graz University of Technology, 8010 Graz, Austria
3
Miba Gleitlager Austria GmbH, 4663 Laakirchen, Austria
*
Author to whom correspondence should be addressed.
Lubricants 2022, 10(5), 103; https://doi.org/10.3390/lubricants10050103
Received: 31 March 2022 / Revised: 12 May 2022 / Accepted: 18 May 2022 / Published: 22 May 2022
(This article belongs to the Special Issue Tribology in Mobility)
Condition monitoring of components in internal combustion engines is an essential tool for increasing engine durability and avoiding critical engine operation. If lubrication at the crankshaft main bearings is insufficient, metal-to-metal contacts become likely and thus wear can occur. Bearing temperature measurements with thermocouples serve as a reliable, fast responding, individual bearing-oriented method that is comparatively simple to apply. In combination with a corresponding reference model, such measurements could serve to monitor the bearing condition. Based on experimental data from an MAN D2676 LF51 heavy-duty diesel engine, the derivation of a data-driven model for the crankshaft main bearing temperatures under steady-state engine operation is discussed. A total of 313 temperature measurements per bearing are available for this task. Readily accessible engine operating data that represent the corresponding engine operating points serve as model inputs. Different machine learning methods are thoroughly tested in terms of their prediction error with the help of a repeated nested cross-validation. The methods include different linear regression approaches (i.e., with and without lasso regularization), gradient boosting regression and support vector regression. As the results show, support vector regression is best suited for the problem. In the final evaluation on unseen test data, this method yields a prediction error of less than 0.4 °C (root mean squared error). Considering the temperature range from approximately 76 °C to 112 °C, the results demonstrate that it is possible to reliably predict the bearing temperatures with the chosen approach. Therefore, the combination of a data-driven bearing temperature model and thermocouple-based temperature measurements forms a powerful tool for monitoring the condition of sliding bearings in internal combustion engines. View Full-Text
Keywords: internal combustion engine; bearing temperature; bearing wear; tribology; lubrication; condition monitoring; data-driven approach; machine learning; regression analysis; model selection internal combustion engine; bearing temperature; bearing wear; tribology; lubrication; condition monitoring; data-driven approach; machine learning; regression analysis; model selection
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MDPI and ACS Style

Laubichler, C.; Kiesling, C.; Marques da Silva, M.; Wimmer, A.; Hager, G. Data-Driven Sliding Bearing Temperature Model for Condition Monitoring in Internal Combustion Engines. Lubricants 2022, 10, 103. https://doi.org/10.3390/lubricants10050103

AMA Style

Laubichler C, Kiesling C, Marques da Silva M, Wimmer A, Hager G. Data-Driven Sliding Bearing Temperature Model for Condition Monitoring in Internal Combustion Engines. Lubricants. 2022; 10(5):103. https://doi.org/10.3390/lubricants10050103

Chicago/Turabian Style

Laubichler, Christian, Constantin Kiesling, Matheus Marques da Silva, Andreas Wimmer, and Gunther Hager. 2022. "Data-Driven Sliding Bearing Temperature Model for Condition Monitoring in Internal Combustion Engines" Lubricants 10, no. 5: 103. https://doi.org/10.3390/lubricants10050103

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