Lubrication regime identification in rolling bearings using local mean decomposition and Stribeck-correlated machine learning

In this article, a physics-based vibro-acoustic method is presented that correlates the physics model (lubrication regime λ), based on the Dowson–Higginson model, with non-stationary time-frequency features (local mean decomposition). A SKF NJ 307 ECP cylindrical roller bearing was subjected to a test under the following conditions: 800 r/min and 2.1 kN, and the following four states were tested along the descending λ trajectory in a run-to-failure test: New/Healthy (λ ≈ 4.2), Transition (λ ≈ 1.0), Boundary (λ ≈ 0.6) and Severe boundary (λ ≈ 0.4). The first four product functions have four features (energy, root mean square (RMS), kurtosis and Shannon entropy) that make a 16-dimensional vector. At the transition, the vibration PF1 kurtosis is maximised at 6.96, and the sound RMS decreases monotonously from 0.412 to 0.252 V. Both support vector machine and Random Forest outperforms the acoustic features (73.7%, 68.4%) and equivalent empirical mode decomposition features (by 11.1 percentage points), reaching 94.4% and 88.9% accuracy under five-fold cross-validation. Through permutation importance analysis, PF3 RMS was observed to be the most discriminative feature (0.142 ± 0.031), suggesting that mid-band product functions are more effective than the more commonly used PF1 kurtosis for the classification of the lubrication state.