Predicting the Remaining Useful Life of Hydraulic Gate Based on Multi-feature Information Fusion
Predicting the remaining useful life(RUL)holds great significance in ensuring the operational safety of complex structures.To enhance the accuracy of RUL prediction for hydraulic gates,we propose a multi-feature in-formation fusion-based approach.Initially,we employ the gamma process to simulate the corrosion evolution of gates and analyze the corrosion-caused degradation of characteristic parameters,including stress,natural vibration fre-quency,and dry/wet modal shapes through numerical simulations.Subsequently,we screen the feature parameters considering monotonicity and discreteness.We construct a health index by fusing these features based on principal component analysis.To model the gate degradation process,we employ a non-linear Wiener process and utilize the particle filtering method to obtain RUL prediction results for the gate at different operating times.Finally,we vali-date the reliability and effectiveness of our proposed method through engineering examples and finite element simu-lations.Our results demonstrate that the fusion of multiple information sources enables a more comprehensive reflec-tion of the gate's degradation state.The root mean square error(RMSE)of the prediction accuracy evaluation in-dex is 1.395 5,the mean absolute error(MAE)is 1.262 8,and the absolute error of variance(VAE)is 0.352 8,showcasing high accuracy.This method can serve as a basis for gate health management and safety assessment.
hydraulic steel gateinformation fusionremaining useful life predictionparticle filter
万方数据
维普
