The acid number evaluates the degree of deterioration of lubricating oil. Existing methods for evaluating the performance degradation of lubricating oils are mostly based on the detection of traditional physical and chemical indicators, which often only reflect a single dimension of the degradation process, thus affecting the accuracy and repeatability of the results. Integrating multi-dimensional information can more comprehensively reflect the essence of degradation, which can improve the accuracy and reliability of the evaluation results. Mid-infrared spectroscopy is an effective means of monitoring the acid number. In this study, a combination of infrared spectroscopy quantitative analysis and chemometrics was used. The oil sample data was divided into training set and validation set by the Kennard–Stone method. In the experiment, a Fourier transform infrared spectrometer equipped with an attenuated total reflection accessory (ATR-FTIR) was used to collect spectral data of the samples in the wavenumber range of 1750–1700 cm⁻¹ (this range corresponds to the characteristic absorption of carboxyl groups and is directly related to the acid number). Meanwhile, a G20S automatic potentiometric titrator was used to determine the acid number as a reference value in accordance with GB/T 7304. The study compared various preprocessing methods. A regression prediction model between the spectra and acid number was established using partial least squares regression (PLSR) within the selected wavenumber range, with the root mean square error of cross-validation (RMSECV), root mean square error of prediction (RMSEP), and coefficient of determination (R) as evaluation indicators. The experimental results showed that the PLSR model established after preprocessing with second derivative combined with seven-point smoothing exhibited the optimal performance, with an RMSECV of 0.00505, an RMSEP of 0.14%, and an R of 0.9820. Compared with the traditional titration method, this prediction method is more suitable for real-time monitoring of production lines or rapid on-site screening of equipment. It can in a timely manner warn of the deterioration trend of lubricating oil, reduce the risk of equipment wear caused by oil failure, and provide efficient technical support for lubricating oil life management.

Strong nonlinear coupling exists between shafting dynamics and bearing lubrication performance in ultra-long multi-support propulsion shafting systems of large ships. This coupling may lead to bearing load redistribution and local oil-film instability. Taking the propulsion shafting system of an actual ship as the research object, this study develops a coupled shafting–bearing quasi-static model. The model considers hull deformation and inertial loads induced by ship motion. A BP neural network is used to represent the nonlinear support characteristics of the bearings and improve the efficiency of multi-condition calculations. The results show that hull deformation and ship-motion accelerations change the bending state of the shafting and the relative position of the journals within the bearings. These changes significantly affect bearing loads, oil-film pressure, minimum oil-film thickness, and journal eccentricity. As the sea state becomes more severe, local bearing load concentration and oil-film deterioration become more pronounced. Some performance parameters may exceed their allowable limits. Bearings at different positions also exhibit different sensitivities to operating conditions and distinct potential failure modes. The proposed method provides a theoretical basis and engineering reference for bearing arrangement, shaft alignment, and safety assessment of ultra-long propulsion shafting systems under complex sea conditions.