Degradation Prediction for Hydraulic Piston Pump Based on Physics-informed Recurrent Gaussian Process

Accurately degradation analysis and prediction for hydraulic piston pump is crucial to ensure hydraulic system reliability, reduce unexpected downtime, and optimize maintenance schedules. The hydraulic piston pump's degradation from wear is typical gradual failure mode. Traditional methods for degradation modelling often rely on physics of failure models or machine learning models. However, physics of failure models may not fully capture the degradation process of the hydraulic piston pump with multiple randomness and uncertainties. Machine learning models generally needs massive degradation data to learn black-box models to reach high accuracy prediction. In order to incorporate the benefits of both methods, a novel physics-informed recurrent Gaussian process model is developed to describe degradation process of hydraulic piston pump and predict remaining useful life. Firstly, the wear process model of three friction pairs including swash plate/slipper, valve plate/cylinder block, and piston/cylinder bore for a type of hydraulic piston pump is investigated. Secondly, the degradation process of hydraulic piston pump is constructed by physics informed recurrent Gaussian process (PI-RGP) model. Comparing with Gaussian process model, recurrent Gaussian process model can reflect time accumulative effect. The mean function of the model is generated by deriving equations from physics of failure model to guide the forecasting process, so that the degradation model is more in line with the actual wear process. In addition, the model can also initiate small data training, and then update and extrapolation the model with new measurements. Finally, the experimental results indicate that the proposed PI-RGP model has foresight of the degradation process and can further improve the degradation prediction accuracy of hydraulic piston pump.

Keywords: Recurrent Gaussian process, Physics-informed, Hydraulic piston pump, Degradation.