An IGA-BP–Based Estimation Method for Water-Film Pressure of Marine Water-Lubricated Bearings

Abstract

Water-film pressure is a key parameter characterizing the load-carrying capacity, frictional performance, and operational stability of water-lubricated bearings; therefore, real-time and accurate monitoring is crucial to the safety of ship propulsion systems. Existing analytical, CFD, and experimental approaches are limited by operating-condition adaptability, computational burden, and cost, while conventional data-driven methods are prone to initialization sensitivity and entrapment in local optima, resulting in inadequate timeliness and accuracy. To address these issues, this study applies a back-propagation (BP) neural network to water-film pressure estimation and introduces an improved genetic algorithm (IGA) with a sine-decay mechanism to optimize the BP network’s weights and biases, thereby constructing an IGA-BP model. Using rotational speed, load, and friction coefficient(COF) as inputs, the model estimates the maximum water-film pressure and its corresponding journal circular angle. Comparative evaluations on multi-condition experimental datasets show that IGA-BP attains an R² of 0.976; relative to BP, the root-mean-square error (RMSE) and mean absolute error (MAE) are reduced by about 53% and 79%, respectively. Compared with GA-BP, RMSE and MAE are further reduced by approximately 34% and 52%, and the model exhibits faster convergence and greater stability. Moreover, against several optimized BP variants, IGA-BP achieves the best overall metrics, offering a useful reference for research on water-lubricated bearings.