基于遗传算法与最大最小原理的故障模式特征选择

在诸如液体火箭发动机等复杂动力学系统的故障诊断中，监控参数组的优选问题一直受到工程技术人员的高度重视。本文提出了综合样本矢量方向离散度概念，以此作为故障特征参数的优选准则；然后利用经过改进的遗传算法，对某液体火箭发动机常见故障的诊断进行了特征参数组的优选。在改进的遗传算法中，采用了非常简洁而高效的染色体编码，针对特征优选的组合优化类问题专门设计了一种特殊的基因迁移算子，并引进了父本个体适应值的动态调整技术与共享函数。数值实验结果表明，该算法具有理想的效果。

Max-Min Principle Based-Selection for the Optimal Feature Parameters in Fault Diagnos is Using Genetic Algorithms

Much inportance has been attachad to the selection of optimal feature parameters subset in the fault diagnosis fields such as liquid rocket propulsion system.This paper presents an effective method of selection for the optimal feature parameters subset using Genetic Algorithms and based on the maximum and minimum clustering criterion for samples,so that the selected feature parameters subset can be used to compose a simplified real time fault classifier with high robustness to various sorts of noises and disturbances.First,a composite directional divergence index for samples is proposed as an evaluation criterion for the selected feature parameters subset for fault diagnosis'purpose;then,Genetic Algorithm has been modified in parts for this specific permutation problem,the dynamic fitness adaptation technique and all sharing function are introduced in order to avoid the population's premature convergence.An ad hoc genetic operator is specially designed to improve the feature selection efficiency.In an addition,all the selection procedures for the optimal feature parameters subset are based on the data set for 16 sorts of common faults simulated for a type of liquid rocket engine system.The numerical experiments show that this selection algorithm is highly effective and the constructed fault classifier with the selected feature parameters possesses morerobustness.