Predicting water content in engine oil using SPA-ISSA-BP methods

Abstract

Contamination of engine oil with water can accelerate oil oxidation and decomposition, resulting in oil degradation. Therefore, the detection and prediction of water content in engine oil is crucial to ensure the long-term safe operation of engines. In this study, near-infrared spectroscopy was used to analyse engine oils with varying water contents. Spectral data were obtained, and various methods, including Savitzky-Golay (SG) smoothing, orthogonal signal correction (OSC), and successive projection algorithm (SPA) were employed to compare the performance differences of principal component regression models. An Improved Sparrow Searches Algorithm (ISSA) was then applied to optimise the Back-Propagation Neural Network (BPNN) for predicting water content in SAE 10W-30 engine oil. The results showed that the performance of the principal component regression model constructed from spectral data after SG smoothing, OSC, and the SPA feature wavelength selection had improved. The BPNN optimised by the improved Sparrow Search Algorithm accelerated the convergence speed of the model and effectively improved the prediction accuracy of the BPNN. The obtained coefficient of determination (R2) was 0.99103, the root mean square error (RMSE) was 2. 2136 10-4, and the mean absolute error (MAE) was 1. 8889×10-4. These results provide an effective method for detecting and predicting the water content in engine oil.