CAJ | 학술논문

为解决液体火箭发动机系统非线性导致的故障诊断困难问题,将免疫Agent(IA)技术应用于液体火箭发动机的故障诊断研究.结合可变半径人工识别球(ARB)方法,在学习人类举一反三的机制,采用最大识别概率原则的基础上,构建了免疫Agent实体,并进行了仿真实验.实验结果表明:该方法训练时间短、正确率高、有较强的抗噪能力和自学习能力,可以应用于液体火箭发动机的状态监测与故障诊断.
위해결액체화전발동궤계통비선성도치적고장진단곤난문제,장면역Agent(IA)기술응용우액체화전발동궤적고장진단연구.결합가변반경인공식별구(ARB)방법,재학습인류거일반삼적궤제,채용최대식별개솔원칙적기출상,구건료면역Agent실체,병진행료방진실험.실험결과표명:해방법훈련시간단、정학솔고、유교강적항조능력화자학습능력,가이응용우액체화전발동궤적상태감측여고장진단.
In order to solve the fault diagnosis difficulties of the Liquid Rocket Engine (IRE) caused by non-linear character, the technique of Immune Agent (IA) was applied to fault diagnosis of IRE. After combining the alterable-radius Artificial Recognition Ball (ARB) and introducing a recognition principle called maximum recognized probability learned from the mechanism of drawing inferences about other cases from one instance, an IA entity was created and applied in the simulation experiment. The results show that this method is efficient with short training time and high rate of correct fault diagnosis. The capability of noise tolerance and learning ability are both powerful. It can work well in the state monitoring and fault diagnosis of the LRE.