摘要
针对航空发动机性能、故障等状态监控的优化问题,以及视情维修的需要,提出了一种混沌时间序列的关联维数提取方法。采用互信息量法和饱和关联维数法,对飞参系统记录的发动机状态参数时间序列的时间延迟和嵌入维数进行了计算,并对时间序列重构相空间,应用G-P算法计算时间序列的关联维数。计算结果表明,发动机状态参数的关联维数为非整数,验证了发动机状态参数具有典型的混沌特性,且关联维数能够正确反映发动机状态参数的特征信息,为发动机状态监控和视情维修提供决策依据。
According to the complexity of aeroengine performance and fault condition monitor and the need of condition maintenance,we presented a correlation dimension analysis method based on the chaos time series.Firstly,the mutual information and saturated correlation dimension methods were respectively applied to calculate the time delay and embedding dimension of the time series of aeroengine condition parameters recorded by flight data recorder,then the phase space of the time series was reconstructed.Correlation dimension was finally obtained by using G-P algorithm.The results show that the correlation dimension on the areoengine condition parameters is non-integer,and the areoengine condition parameters have typical chaotic characteristics,and the correlation dimension can correctly reflect the characteristic information of aeroengine condition parameters and can be the basis for decision-making of aeroengine condition monitor and condition maintenance.
出处
《计算机仿真》
CSCD
北大核心
2013年第9期56-59,共4页
Computer Simulation
关键词
混沌
航空发动机
飞参数据
相空间重构
关联维数
Chaos
Aeroengine
Flight data
Phase space reconstruction
Correlation dimension
