Fuzzy Identification of Lubrication Degradation State Based on Multi-Index Fusion

Lubrication failure has become a predominant failure mode in aviation roller bearings. Timely identification of lubrication degradation is critical for preventing premature bearing failure. This paper presents a fuzzy identification method of lubrication degradation stages by fusing multiple indicators. Firstly, four monitoring indicators, including the oil film thickness (OFT), wear surface roughness (WSR), contact resonance frequency (CRF), and amplitude of CRF (CRFA), are extracted through numerical simulations to characterize the lubrication degradation process. Then, a fuzzy evaluation method is proposed to identify the lubrication degradation stages by integrating these indicators. The results indicate that these four indicators can identify three typical stages of the lubrication degradation process—good lubrication, normal wear, and severe wear, with an accuracy rate exceeding 92%. Finally, lubrication degradation experiments are carried out on a sliding-rolling test rig to verify the method’s effectiveness. This work provides superior interpretability of the multifactorial coupled lubrication degradation process analysis.