Wear Prediction Algorithm for Feedback Ball Head of Servo Valve

Wear of the feedback ball head–ball seat interface changes the contact state and reduces the feedback force in electro-hydraulic servo valves, resulting in output nonlinearity and performance degradation. Existing wear models usually assume fixed surface morphology parameters, which their ability to describe time-varying wear evolution during repeated sliding. To address this issue, this study proposes a hybrid wear-prediction framework integrating fractal contact theory, Archard wear law, Gaussian process regression, and a servo-valve mechanical model. The real contact area and wear coefficient are expressed as functions of fractal parameters, while Gaussian process regression is used to predict their evolution under different loading cycles and displacement loads. Repeated loading tests and white-light interferometry measurements were performed to validate the proposed method. The results show that the fractal dimension of the ball seat increased by approximately 4.01%, whereas that of the ball head decreased by approximately 1.71%. After about 12,000 cycles, the fractal parameters tended to stabilize. The prediction error of the Gaussian process regression model was below 3%, and the wear-depth prediction error remained within approximately 1%. These results indicate that the proposed method can effectively capture the time-varying sliding wear behavior of the feedback ball head–ball seat interface.