基于遗传算法与Shannon熵的故障监控参数优选

研究液体火箭发动机故障诊断中监控参数的优选问题.基于Shannon熵理论提出 了特征参数组所含故障分类信息的理论值及其工程计算方法,证明了故障分类信息与参数相 关性之间的单调降关系,并以此作为特征参数的优选准则,利用改进的遗传算法对某液体火 箭发动机的常见故障进行了特征参数优选,数值实验结果表明所选特征参数合理,且故障分 类器的计算复杂度大大降低而对噪声的鲁棒性大大提高.

SHANNON'S ENTROPY BASED-SELECTION FOR THE OPTIMAL FEATURE PARAMETERS IN FAULT DIAGNOSIS USING GENETIC ALGORITHMS

In this paper,an approach to select feature parameters in fault diagnosis of liquid rocket engine is presented.Based on Shannon's theory on information entropy,theoretical fault chassification information contained in the selected parameters is given,its corresponding engineering calculation formula is then presented as a criterion of feature parameter selection.The modified genetic algorithm is used to select the optimal parameters for the common faults of a liquid rocket engine.The numerical experiment shows that the selected parameters are reasonable and the fault classifier constructed with them is of much less computational complexity and more robust to noises and disturbances.