Correlative Method for Diagnosing Gas-Turbine Tribological Systems

Abstract

Lubricated tribosystems such as main-shaft bearings in gas turbines have been successfully diagnosed by oil sampling for many years. In practice, the interpretation of wear debris analysis results can pose a challenge due to the intricate structure of power transmission systems and the varying degrees of sensitivity among test methods. In this work, oil samples acquired from the fleet of M601T turboprop engines were tested with optical emission spectrometry and analyzed with a correlative model. Customized alarm limits were determined for iron by binning aluminum and zinc concentration into four levels. Two-way analysis of variance (ANOVA) with interaction analysis and post hoc tests was carried out to study the impact of aluminum and zinc concentration on iron concentration. A strong correlation between iron and aluminum, as well as a weaker but still statistically significant correlation between iron and zinc, was observed. When the model was applied to evaluate a selected engine, deviations of iron concentration from the established limits indicated accelerated wear long before the occurrence of critical damage. Thanks to ANOVA, the assessment of engine health was based on a statistically proven correlation between the values of the dependent variable and the classifying factors.