摘要
民用航空发动机故障诊断的方法很多 ,而各种方法在具体操作过程中有着不同的作用和意义。本文提出一种依靠人工神经网络方法研究民航发动机各种故障征兆影响因素的作用程度 ,并利用其仿真数据分析 ,找出具体工况过程中作用相对较大的因素。从而在适时与视情维修过程中提出合理的建议 ,以便为相应维修方法的实施。
A great number of methods have applied to Fault Diagnosis of Turbofan Engine , while presently contribution and function of each way are different in the specific process of operation. Consequently this paper provide a theory to study effective degree of turbofan engine fault symptoms depending on Artificial Neural Network methodology with simulation data, and analyze these data to find out the relative outstanding factors, of important contribution to fault, by mathematical model about optimum allocation of resource. Thereby, we may give some reasonable suggestions and advice in the procedure of timely engine status monitoring and maintenance based on case, so as to raise the reliant evidence for actualization, reformation and further research application of maintenance.
出处
《计算机仿真》
CSCD
2003年第4期40-43,共4页
Computer Simulation
