Prediction life of lubricants on the analysis of experimental data on their optical density

Шрам, В. Г.; Агафонов, Е. Д.; Lysyannikov, N. N.; Lysyannikov, А. V.; Ковалева, М. А.

doi:10.1088/1742-6596/1399/5/055009

Cited by 1 publication

(1 citation statement)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Therefore, this approach's success opens up wide potential to minimize the time and money associated with trial and error, which could strengthen the economy to a great extent [15]. At the current status quo, many tribological studies are devoted to the estimation of the marginal life of lubricants [16][17][18][19]. In practice, the prediction of the limit state for a lubricant is dependent on the manufacturer's recommendation.…”

Section: Introductionmentioning

confidence: 99%

Recent Progress of Machine Learning Algorithms for the Oil and Lubricant Industry

2023

View full text Add to dashboard Cite

Machine learning (ML) algorithms have brought about a revolution in many industries where otherwise operation time, cost, and safety would have been compromised. Likewise, in lubrication research, ML has been utilized on many occasions. This review provides an in-depth understanding of seven ML algorithms from a tribological perspective. More specifically, it presents a comprehensive overview of recent advancements in ML applied to lubrication research, organized into four distinct categories. The first category, experimental parameter prediction, highlights the significant contributions of artificial neural networks (ANNs) in accurately forecasting operating conditions related to friction and wear. These predictions offer valuable insights that aid in forensic preparation. Discriminant analysis, Bayesian modeling, and transfer learning approaches have also been used to predict experimental parameters. Second, to predict the lubrication film thickness and identify the lubrication regime, algorithms such as logistic regression and ANN were useful. Such predictions provide up to 99.25% accuracy. Third, to predict the friction and wear for a given experimental condition, support vector machine (SVM), polynomial regression, and ANN offered an accuracy above 93%. Finally, for condition monitoring for bearings, gearboxes, gear trains, and similar critical situations where regular in-person inspection is difficult, Naïve Bayes, SVM, decision trees, and ANN were utilized to predict the safe life of lubricants. This review highlighted these four aspects with state-of-the-art examples and discussed the current situation and projected future possibilities of lubricant design facilitated by ML techniques.

show abstract