遗传优化神经网络在气密性检测中的应用

Application of genetic optimization-based neural network in air leak detection

气密性检测在保证产品质量和性能上起着很重要的作用,其检测准确度受温度、测试压力、容器体积、平衡时间等多个因素的影响;容器内气体运动状况复杂,难以建立泄漏量与各影响因素之间精确的函数关系,无法直接补偿检测结果的误差。提出了基于遗传优化的神经网络方法,建立了以多影响因素为输入量、泄露量为输出量的模型,并对实测数据进行处理,补偿检测结果的误差。通过实验与仿真验证了该方法的可行性,提高了气密性检测的准确度。

Air leak detection plays an important role in ensuring the quality and performance of the product. The detection accuracy is influenced by temperature, test pressure, container size, balance time and so on. The gas movement status of the container is complicated and is difficult to be expressed by accurate function, so it is hard to compensate the error directly. Neural network based on genetic optimization method is proposed. The mathematical model of the relationship between the leakage and influence factors is established. Using this model, the test data is processed, the error of the leakage is compensated. The feasibility is verified through experiments on the air leak detection platform based on differential pressure method and simulations. The accuracy of the air leak detection is increased.