Abstract
Hydrodynamic journal bearings are used within a wide range of machines, such as combustion engines, gas turbines, or wind turbines. For a safe operation, awareness of the lubrication regime, in which the bearing is currently operating, is of great importance. In the current study, highspeed data signals of a torque sensor, sampled with a frequency of 1000 hz in a time range of 2.5 s, obtained on a journal bearing test-rig under various operating conditions, are used to train machine learning models, such as neural networks and logistic regression. Results indicate that a fast Fourier transform (fft) of the highspeed torque signals enables accurate predictions of lubrication regimes. The trained models are analysed in order to identify distinctive frequencies for the respective lubrication regime.
Highlights
Hydrodynamic journal bearings are an important part of various machinery components due to their excellent damping, usability at high speeds and their straightforward setup
888 high-speed torque measurements taken from various experiments, which were carried out on a journal bearing test-rig, were classified regarding their lubrication regime
Machine learning methods were trained on training data and their accuracy was evaluated on a test dataset
Summary
Hydrodynamic journal bearings are an important part of various machinery components due to their excellent damping, usability at high speeds and their straightforward setup. The failing mechanisms of journal bearings are well understood and often connected with seizure, which leads to a rapid fault, wear, fatigue or overheating. State-of-the-art journal bearings are composed of heterogeneous materials in order do fulfil demands depending on the area in which they are used [3,4]. Lubrication regimes, which are classified into boundary, mixed and fluid lubrication, are paramount to the current condition of a journal bearing system and to carry out predictions regarding wear, friction and failure likelihood. In any machinery equipment rapid failure of the system should be avoided, in order to guarantee a safe and cost efficient operation [5]. Failures in machine elements can in many cases be linked to an altered system behaviour, which can be measured with specialised measurement equipment
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