An integrated knowledge and data model for adaptive diagnosis of lubricant conditions

Lubricant condition diagnosis often encounters conflicting conclusions due to the reverse degradation of indicators and coupling failures. To address this issue, a knowledge-guided adaptive diagnosis (KG-ADLC) method is developed with expert knowledge. Based on lubricant indicators in three attributes, a confidence-driven failure inference mechanism is developed by integrating rule-based reasoning with evidence theory. Furthermore, the KG-ADLC model is established that employs the inference mechanism to guide the synchronous evaluation of lubricant failures. For verification, the developed model is tested with the samples from the simulation experiment and authentic aero-engines. Experimental results reveal that the constructed model can accurately identify six types of lubricant conditions. Notably, the average accuracy has been improved from 74.8 % to 93.7 % when compared to existing methods.