Prediction of RUL of lubricating oil based on Deep Learning Technique

This study uses condition monitoring (CM) to determine the lubricating oil's remaining usable life (RUL). First, Atomic emission spectroscopy (AES) was used to determine the lubricating oil's element makeup and content. Under use were quantitatively examined. The precision and efficacy of the RUL prediction model might be diminished in light of the wide range of oil data acquired using AES. A thorough it is recommended to screen oil data using a method for choosing parameters based on correlation analysis, lubricant degradation analysis, and information entropy. In order to address this issue. The lubricant's RUL prediction model was then developed using a support vector machine (SVM). It is shown that the SVM prediction model's efficacy and accuracy by contrasting the experimental findings with the prediction model's output data.