基于极度梯度提升模型的火炮身管寿命预测

Life Prediction of Gun Barrel Based on Extreme Gradient Boosting Model

为提高火炮身管寿命预测的精度,将身管内径磨损量作为寿命预测指标,提出基于极度梯度提升(XGBoost)模型的火炮身管寿命预测算法。以火炮弹射数为输入,身管内径磨损量为输出,通过集成多个弱学习器反复训练来拟合前一个弱学习器预测值与实际值之间的残差,从而生成强学习器,并通过在损失函数后加入正则化项及采用剪枝技术降低模型过拟合的风险。基于某型火炮实测数据进行验证,结果表明:该模型不仅有效解决了火炮弹射量与身管内径磨损量之间的映射关系,且相比支持向量机、BP神经网络、灰色模型等现有算法显著提升了身管寿命预测精度。

In order to improve the accuracy of barrel life prediction,a barrel life prediction algorithm based on extreme gradient boost(XGBoost)model was proposed by taking the wear of barrel inner diameter as the life prediction index.Taking the number of artillery shells as the input and the barrel inner diameter wear as the output,the strong learner was generated by integrating multiple weak learners to fit the residual between the predicted value and the actual value of the previous weak learner,and the risk of model overfitting was reduced by adding a regularization term after the loss function and using pruning technology.The model is verified based on the measured data of a certain type of artillery,and the results show that the model not only effectively solves the mapping relationship between the artillery ejection quantity and the barrel inner diameter wear,but also significantly improves the barrel life prediction accuracy compared with the existing algorithms such as support vector machine,BP neural network and gray model.