Attribute-driven Fuzzy Fault Tree Model for Adaptive Lubricant Failure Diagnosis

Lubricant diagnosis serves as a crucial accordance for condition-based maintenance (CBM) involving oil changing and wear examination of critical parts in equipment. However, the accuracy of traditional end-to-end diagnosis models is often limited by the inconsistency and random fluctuations in multiple monitoring indicators. To address this, an attribute-driven adaptive diagnosis method is developed, involving three attributes: physicochemical, contamination, and wear. Correspondingly, a fuzzy fault tree (termed FFT)-based model is constructed containing the logic correlations from monitoring indicators to attributes and to lubricant failures. In particular, inference rules are integrated to mitigate conflicts arising from the reverse degradation of multiple indicators. With this model, the lubricant conditions can be accurately assessed through rule-based reasoning. Furthermore, to enhance its intelligence, the model is dynamically optimized with lubricant analysis knowledge and monitoring data. For verification, the developed model is tested with lubricant samples from both the fatigue experiment and actual aero-engines. Fatigue experiments reveal that the proposed model can improve the lubricant diagnosis accuracy from 73.4% to 92.6% compared with the existing methods. While for the engine lubricant test, a high accuracy of 90% was achieved.