Classification of operational states in porous journal bearings using a semi-supervised multi-sensor Machine Learning approach

Abstract

Tribosystems are designed for long-lasting operation necessitating condition monitoring to identify critical regimes in time. On the example of porous journal bearings we propose a new approach for the classification of the operational states.Tribometer data from accelerated lifetime tests were used for developing a semi-supervised multi-sensor Machine Learning (ML) classifier. To advance the state-of-the art and gain higher classification accuracy, data from multiple sensors were combined by calculating time and frequency feature sets. The frequencies found in the sensor data were traced using a 3D scanning vibrometer and originated from eigenmodes and vibration states of the system. Relevant features were selected using Sequential Forward Floating Selection (SFFS) to enhance the performance of the model. Labeling was assisted by explorative data analysis techniques, i.e., principal component analysis (PCA) and subsequent hierarchical (Ward) clustering of the feature sets.A tree-based ensemble classifier (Extra-Trees) differentiating four tribological regimes from ‘Run-in’ to ‘Critical’ yielded a classification accuracy of 0.98. The algorithm was able to reliably detect states even when applied to data from previously unseen experiments and to predict terminal failure of a bearing up to 50 h before it occurred.