摘要
为在武器系统故障发生前实现预测、实现装备的视情维修,开展基于遗传神经网络的故障预测技术研究。采用实数编码方式和自适应的交叉率、变异率改进遗传算法,并将改进遗传算法用于神经网络的权重学习得到遗传神经网络。利用监测到的装备特征参数数据进行网络训练,然后将遗传神经网络预测装备特征参数的退化趋势。预测实例表明遗传神经网络可在故障发生前实现故障预测,较基本神经网络有较大性能改善,可提高武器装备的保障能力,实现视情维修。
To forecast the fault and carry out condition-based maintenance for weapon system,the prognostic method based on Genetic Neural Network（GNN） is studied.The genetic algorithm is improved by adopting real coding,adaptive crossover rate and mutation rate,also the learning algorithm of neural network＇s weight is ameliorated with the improved genetic algorithm,and the genetic neural network is obtained.The genetic neural network is trained by the detected data of equipments,and then is used to predict the degenerating trend of the characteristic parameters of the equipments.The predicting example shows that the use of the improved neural networks can achieve fault prediction before the time point of faults respectively,and the predicting accuracy and the predicting performance of the genetic neural networks are greatly improved compared with those of the basic neural network,which can enhance the supporting capability of the weapon equipment and realize condition-based maintenance.
出处
《空军工程大学学报（自然科学版）》
CSCD
北大核心
2011年第1期15-19,共5页
Journal of Air Force Engineering University(Natural Science Edition)
基金
国家"863"计划资助项目(2009AAXXX06)
关键词
故障预测
遗传神经网络
遗传算法
视情维修
fault prediction
genetic neural network
genetic algorithm
condition-based maintenance
