A Hyperspectral Analysis-Based Approach for Estimation of Wear Metal Content in Lubricating Oil

Lubricating oil reflects mechanical component aging and wear. Accurate quantification of its wear metals is essential for equipment safety and intelligent maintenance. This study introduces a rapid, non-destructive method for detecting wear metal content in lubricating oil using hyperspectral technology to overcome limitations such as bulky, expensive instruments and destructive testing in current spectroscopic techniques. Absorption spectra of 98 marine gearbox oil samples were acquired using Hach UV-Vis and GLT optical fiber spectrometers. We propose a multi-head attention mechanism enhanced genetic algorithm (MHA-GA) for deep feature extraction, integrating attention weights into band selection and fitness evaluation to identify key features under multi-element interference. Wear metal prediction models were constructed using random forest (RF), support vector regression (SVR), and extreme gradient boosting (XGBoost). Results demonstrate MHA-GA outperformed traditional genetic algorithm (GA) and competitive adaptive reweighted sampling (CARS) in feature selection. The MHA-GA-XGBoost model performed best. Fe prediction R2 reached 0.96 (Hach) and 0.93 (GLT), with RPDs of 5.33 and 3.90. For Cu, R2 reached 0.91 and 0.83, with RPDs of 3.35 and 2.42. The results indicate that hyperspectral technology combined with machine learning enables effective non-destructive wear metal quantification, offering a promising strategy for intelligent maintenance and condition monitoring of lubricating oil.