Application of Machine Learning Method to Predict Reliability in Lubricating Oil System Components

Abstract

In predicting the reliability and failure of components, classical methods are often used by determining the distribution between failure times. Sometimes, the determination of this distribution does not always match the data pattern that is owned because of the limited data records and the many types of distribution that must be chosen. In addition, how much influence the time series has on components cannot be clearly analyzed. Therefore, in this study a prediction will be carried out by combining classical methods with machine learning, namely Support Vector Regression (SVR) and Least Square Support Vector Regression (LSSVR). Both methods are considered capable of improving the prediction accuracy of a series of data. The results showed that the classical combined method with LSSVR had better accuracy than SVR.

Enhancing oil pressure dynamics in a high-performance racing engine with innovative lubrication system tuning

“…Efficient lubrication enhances the engine's efficiency, reduces energy losses, and extends its operational life [10,11]. Engine lubrication systems can be broadly classified into two main types: wet sump and dry sump systems [12,13].…”