Detection of Lubrication Condition in Hydrodynamic Journal Bearings Based on Dynamic Experimentation Using Acoustic Emission and Machine Learning

Reliable detection of lubrication conditions in sliding bearings is crucial for condition monitoring and predictive maintenance. Despite advances in tribological research, there remains a need for accurate diagnostics that indicate worsening of lubricity in mixed and boundary lubrication states. In this study, a dynamic test procedure is utilised to classify lubrication conditions with the help of a boosted tree classification algorithm. A radial journal bearing test rig is built and equipped with a high-frequency acoustic emission (AE) sensor on which experiments consisting of repeated dynamic speed and load alterations are conducted. AE signal features are extracted, compared and used to train an Extreme Gradient Boosting (XGBoost) classification model. The model achieves high accuracy (97.57%) in distinguishing adequate vs. starved lubrication conditions in mixed friction. Misclassifications are mainly observed at the lowest load or speed conditions, where residual lubrication effects make the classes less separable. The model’s generalisability is evaluated by applying it to tests with differing viscosity classes and alternative bearing materials without retraining, with the classifier retaining good performance. The model is also used to detect anomalies in a grease-lubricated system, where it successfully detects poor lubrication conditions. While it is known prior to this publication that AE is a good tool to detect anomalous behaviour in hydrodynamic journal bearings, the findings presented highlight the potential for the transferability of anomaly detection models trained in a laboratory setting and applied to different real-world applications to reduce life-cycle maintenance costs and increase uptime in industrial applications.