Hybrid methodology development for lubrication regimes identification based on measurements, simulation, and data clustering

The lubrication regimes of a laboratory scale journal bearing were analysed with wide band acoustic emission (AE) measurements. The analysis was supported by data-based clustering of AE data. Digital twin of the journal bearing was generated with Simpack multi-body simulation software to study opportunities for developing a hybrid methodology for more complex systems monitoring. The AE and data-based clustering approach can be effectively used to reveal fundamental lubrication modes, i.e., hydrodynamic (HL), mixed (ML) and boundary (BL) lubrication as a function of Hersey number, which can be evaluated in situ by utilizing digital twin. Besides AE the other parameters monitored were friction torque, bearing temperature, loading, sliding velocity and oil pressure. The materials used in the experiments were case-hardened 18CrNiMo7-6 steel and nitrided 42CrMo7 steel. The tests were lubricated with synthetic extreme-pressure gear oil (SGN 320) and the bearing temperature was kept constant during the tests. The bearing loading and sliding velocity during tests were varied in the wide range resulting in different lubrication situations. The acoustic emission signals power and frequency content was analysed, and essential features were extracted for data clustering. For lubrication regime change identification the parameters such as signal RMS and coefficient of variation (CV) proved to be important, while signal kurtosis showed to be the most sensitive in discovering anomalies. The high sensitivity requires data filtering to remove erroneous peaks and to reveal real trends more clearly. It is also interesting to notice the changes in AE frequency during changing to different lubrication regime. In literature different clustering and classification methods has been proposed and applied for journal bearing status identification. Here the selected unsupervised clustering method was the mean-shift clustering due to fact, that the lubrication regimes in the Stribeck curve form an inseparable continuum. The algorithm does not require specifying the number of clusters in advance, i.e., the clusters are determined by the algorithm with respect to the data. The results were compared to simulations with digital twin. i.e., by comparing simulated digital twin film thickness and measured AE kurtosis relative to measured Hersey number. It was concluded that digital twins can be utilized as virtual sensor for in situ detecting of lubrication regimes, if it is possible to calibrate the simulation with sensitive measurements, e.g., AE. It is obvious that simulations alone cannot predict suddenly appearing anomalies, such as impurities or surface fatigue failures.