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- Title
Machine-Learning-Based Wear Prediction in Journal Bearings under Start–Stop Conditions.
- Authors
König, Florian; Wirsing, Florian; Singh, Ankit; Jacobs, Georg
- Abstract
The present study aims to efficiently predict the wear volume of a journal bearing under start–stop operating conditions. For this purpose, the wear data generated with coupled mixed-elasto-hydrodynamic lubrication (mixed-EHL) and a wear simulation model of a journal bearing are used to develop a neural network (NN)-based surrogate model that is able to predict the wear volume based on the operational parameters. The suitability of different time series forecasting NN architectures, such as Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Nonlinear Autoregressive with Exogenous Inputs (NARX), is studied. The highest accuracy is achieved using the NARX network architectures.
- Subjects
MACHINE learning; JOURNAL bearings; TIME series analysis; SIMULATION methods &; models; FORECASTING
- Publication
Lubricants (2075-4442), 2024, Vol 12, Issue 8, p290
- ISSN
2075-4442
- Publication type
Article
- DOI
10.3390/lubricants12080290