Work

Machine-Learning-Based Wear Prediction in Journal Bearings under Start–Stop Conditions

Year: 2024

Type: article

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.

Source: Lubricants

DOI: 10.3390/lubricants12080290

Authors: Florian König, Florian Wirsing, Ankit Singh, Georg Jacobs

Institution: RWTH Aachen University

Language: English

Topic: Tribology and Lubrication Engineering

Subfield: Mechanical Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,955

Funders: Australian Government, Bundesministerium für Bildung und Forschung