A fire control system fault prediction method based on IBKA-GBDT
The fire control system is an essential element of tank operations,greatly enhancing battlefield survivability and tactical efficiency through precise strikes,swift responses,and all-conditions support.As such,accurately predicting faults within this system is crucial.To enhance fault prediction accuracy and reduce operational costs,a model prediction method based on a hybrid strategy-improved Black-winged Kite Algorithm to optimize the Gradient Boosting Decision Tree(GBDT)is proposed.The grey relational analysis method is used to process the raw data to reduce data redundancy and dimensionality,and highly correlated attributes are selected to construct the dataset.Logistic chaotic mapping,spiral search strategy,and triangular walk strategy are introduced to improve the Black-winged Kite Algorithm,further optimizing the key parameters of the Gradient Boosting Decision Tree and constructing a fault prediction model to achieve fault prediction for the predicted data.Additionally,signal data collected from the fire control system's electrical component test bench was used as the experimental subject,setting the same parameters to conduct comparative experiments with traditional gradient boosting decision trees,whale optimization algorithms,and Black-winged Kite optimized gradient boosting decision tree models.Experimental results demonstrate that this method can quickly and accurately predict faults in the processed dataset,achieving an average accuracy rate of 96.74％,providing a crucial basis for subsequent maintenance and repair of the fire control system.
fire control systemfault predictiongradient boosting decision treegrey relational analysisblack-winged kite algorithm
万方数据
维普
