Unlocking the Potential of Soft Computing for Predicting Lubricant Elemental Spectroscopy

Abstract

Predictive maintenance of mechanical systems relies on accurate condition monitoring of lubricants. This study assesses the performance of soft computing models in predicting the elemental spectroscopy (Fe, Pb, Cu, Cr, Al, Si, and Zn) of engine lubricants, based on the electrical properties (ε′, ε″, and tan δ) of oil samples. The study employed a dataset of 49 lubricant samples, comprising elemental spectroscopy and dielectric properties, to train and test several soft computing models (RBF, ANFIS, SVM, MLP, and GPR). Performance of the models was evaluated using error metrics such as MAPE, RMSE, and EF. The RBF model delivered the most accurate predictions for silicon at 7.4 GHz, with an RMSE of 0.4 and MAPE of 0.7. Performance was further improved by fine-tuning RBF parameters, such as the hidden size and training algorithm. The sensitivity analysis showed that utilizing all three input electrical properties (ε′, ε″, and tan δ) resulted in the lowest errors. Nevertheless, there are limitations to the study. In our country, measuring the electrical properties of engine lubricants and equipment is not a common practice, which leads to a limited number of samples studied. Despite these limitations, this study offers a proof-of-concept for predicting lubricant conditions based on readily measurable electrical properties. This paves the way for developing machine learning-based real-time lubricant monitoring systems.

Comparative Analysis of Soft Computing Models for Predicting Viscosity in Diesel Engine Lubricants: An Alternative Approach to Condition Monitoring

“…Performance was further improved by fine-tuning RBF parameters, such as the hidden size and training algorithm. 45 These results show the potential of ML models to accurately predict engine lubricant properties and aid in impressive maintenance strategies. In this study, the RBF model was able to predict the viscosity values through EWH, Cr, Pb, Sn, Al, Mo, Na, B, V, Mg, Ba, Ca, P, and Zn even at a training size of 50%.…”

“…Consequently, these algorithms stand as invaluable tools for tackling complex real-world challenges. 45,65,66 The SVM algorithm stands out as a popular and effective tool for predicting continuous values across diverse scientific…”

“…Consequently, these algorithms stand as invaluable tools for tackling complex real-world challenges. 45,65,66 The SVM algorithm stands out as a popular and effective tool for predicting continuous values across diverse scientific 1 objective developing an efficient machine learning (ML) algorithm for the prediction of nanofluid viscosity.…”

“…They excel in handling nonlinear problems, navigating through uncertain scenarios, adapting to dynamic conditions, emulating human-like decision-making processes, boasting a versatile range of applications, and demonstrating efficiency and accuracy. Consequently, these algorithms stand as invaluable tools for tackling complex real-world challenges. ,, …”

“…Ultimately, through our analysis, the RBF model has been established as the most reliable predictor when it comes to determining the lubricant properties (Fe, Pb, Cu, Cr, Al, Si, and Zn) based on the electrical properties (ε′, ε″, and tan δ) at a frequency of 7.4 GHz. 45 Based on the aforementioned context, the aim is to evaluate the feasibility of predicting the numerical value of lubricant viscosity to eliminate the need for an independent test to monitor the condition of the lubricant viscosity. The following section will elaborate on the research objectives and the approaches taken to achieve them.…”