Abstract
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.
Highlights
Lubricants constitute essential construction components for com bustion engines and contribute to the strict requirements regarding the emissions, performance and efficiency of modern engines
We provide a comprehensive comparison of four combinations for sample preparation by examining the analytical performance with various used engine oil samples of different engine types
While it may be possible to obtain practicable results with simpler chemometric algorithms when dealing with just one engine type, we considered to focus on the selected machine learner to obtain a more robust and ver satile model which can more deal with engine data not included within the original dataset
Summary
Lubricants constitute essential construction components for com bustion engines and contribute to the strict requirements regarding the emissions, performance and efficiency of modern engines Their main tasks include reducing wear of the moving parts as well as transmitting thermal stresses while keeping their viscosity, contributing to a longer lifetime and reliable operation of the machine. Standard oil analytics include a wide range of parameters such as the viscosity, total base number, particle size of debris, soot content as well as its elemental composition [6,7] As for the latter, there are three main groups of elements to be differenti ated (see Fig. 1): Additives (e.g. Ca, P, Zn) [8,9], wear elements (e.g. Al, Cu, Fe) and contaminants (e.g. K, Na, Si). They can provide a characteristic ‘fingerprint’ that allows for distinguishing specific types of oil spectroscopically [13,14]
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