基于最小二乘支持向量机的机枪加速寿命建模

Accelerated Life Modeling for Machine Gun Based on LS-SVM

加速寿命试验可以在短时间内对产品寿命进行有效评定。针对以往机枪加速寿命模型预测能力较差的问题,提出了基于最小二乘支持向量机(LS-SVM)建立加速寿命模型的方法。以机枪寿终射弹量为寿命特征,以试验环境温度、枪管最大温度、射击间隔时间以及最大膛压为加速应力建立了机枪加速寿命模型。由于LS-SVM的参数选取是决定建立模型优劣的关键因素,因此采用遗传算法对LS-SVM参数进行优化选取。通过分析比较LS-SVM与常规变换方法和BP神经网络建立的机枪加速寿命模型精度,结果表明利用LS-SVM方法建立的模型明显优于其他2种方法,验证了LS-SVM在机枪加速寿命预测应用中的有效性。

Product life can be assessed effectively in the short period of time by using accelerated life test. Aimed at problems resulted from poor predictive ability of the previous accelerated life model, a method to establish accelerated life model for machine guns based on LS-SVM was proposed. It took the machine gun＇s shooting ammunition quantity before the life end as the life feature and selected the test ambient temperature, barrel＇s maximum temperature, shooting interval, maximum pressure in bore as the accelerated stresses. A genetic algorithm was adopted to determine the optimal parameters of LS- SVM. The prediction results show that the model established in this paper are better than the general transformation and BP neural network models obviously, and the LS-SVM method are effective on acceler- ated life prediction for machine guns.