Wear diagnosis method for key friction pairs of high-strengthened diesel engines based on random forest method
LIU Jie
FENG Haibo
LIU Fengchun
XIE Jun
CHEN Chuang
DONG Hongxia
LI Chuang
MAO Yuxin
Abstract：To clarify the friction pair wear information contained in diesel engine lubricating oil,spectral analysis is performed on 170 groups of lubricating oil samples collected during the durability test of a high-strengthened diesel engine.The nonlinear fitting method is adopted to analyze the correlation between the mass fractions of Fe,Cu,and Al elements in the oil and the wear of key friction pairs.A Python programming software is used to construct a random forest prediction model based on Fe element,and the receiver operating characteristic(ROC)curve is combined to evaluate the accuracy of the model.The results show that the mass fractions of Fe,Cu,and Al elements,which are mainly correlated with the wear of key friction pairs,are significantly concentrated in specific intervals.The mass fractions of Fe-Al elements can be used to correlate the wear of piston-piston ring-cylinder liner friction pairs,while the mass fractions of Fe-Cu elements can be used to correlate the wear of crankshaft-crankshaft bearing friction pairs.The normal wear interval and wear alert interval are set according to the mass fractions of Fe,Cu,and Al elements,and the accuracy of the constructed Fe-element random forest prediction model reaches 88.24％.There is a strong nonlinear correlation among the three main metal elements of Fe,Cu,and Al,and the wear alert interval can be used as a supplementary basis for diagnosing abnormal wear of piston-piston ring-cylinder liner and crankshaft-crankshaft bearing friction pairs in high-strengthened diesel engines.
Keywords：friction pairsspectral analysiswear intervalwear alertnonlinear correlationROC
Publication Date：2025-11-30
Online Publishing Date：2026-05-22(First online date of this platform, not the publication date of the document)
Pages：7( 58-64 )