Wear Metal Analysis of Oils

Correlative Method for Diagnosing Gas-Turbine Tribological Systems

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.

Extraction Induced by Emulsion and Microemulsion Breaking for Metal Determination by Spectrometric Methods - A Review

This review focuses on extraction induced by the destabilization of emulsified systems combined with spectrometric techniques for metal analysis in oily samples. This approach is based on the formation and breaking of an emulsion (extraction induced by emulsion breaking - EIEB) or microemulsion (extraction induced by microemulsion breaking - EIMB) to transfer the analytes from the oil sample to the aqueous phase, which is separated in the process. Its simplicity, speed, and low cost have contributed to its growing popularity among researchers. However, the potential of EIEB and EIMB is far from being fully exploited. Therefore, this paper aims to provide relevant information to expand the applicability of these methods. The principle of the methods is discussed, and a brief description of emulsified systems is presented. The parameters affecting the extraction efficiency and calibration strategy are also critically discussed. Furthermore, the analytical applications of the methods are reviewed. Trends and opportunities in this field are also considered.

Analyses of used engine oils via atomic spectroscopy - Influence of sample pre-treatment and machine learning for engine type classification and lifetime assessment

The analysis of used engine oils from industrial engines enables the study of engine wear and oil degradation in order to evaluate the necessity of oil changes. As the matrix composition of an engine oil strongly depends on its intended application, meaningful diagnostic oil analyses bear considerable challenges. Owing to the broad spectrum of available oil matrices, we have evaluated the applicability of using an internal standard and/or preceding sample digestion for elemental analysis of used engine oils via inductively coupled plasma optical emission spectroscopy (ICP OES). Elements originating from both wear particles and additives as well as particle size influence could be clearly recognized by their distinct digestion behaviour. While a precise determination of most wear elements can be achieved in oily matrix, the measurement of additives is performed preferably after sample digestion. Considering a dataset of physicochemical parameters and elemental composition for several hundred used engine oils, we have further investigated the feasibility of predicting the identity and overall condition of an unknown combustion engine using the machine learning system XGBoost. A maximum accuracy of 89.6% in predicting the engine type was achieved, a mean error of less than 10% of the observed timeframe in predicting the oil running time and even less than 4% for the total engine running time, based purely on common oil check data. Furthermore, obstacles and possibilities to improve the performance of the machine learning models were analysed and the factors that enabled the prediction were explored with SHapley Additive exPlanation (SHAP). Our results demonstrate that both the identification of an unknown engine as well as a lifetime assessment can be performed for a first estimation of the actual sample without requiring meticulous documentation.