基于证据理论的航空发动机状态评估

Aero-engine condition assessment based on evidence theory

针对航空发动机状态监测数据的参数多、不确定性和冲突性,将航空发动机作为研究对象,构建了基于D-S证据理论与RBF神经网络相结合的健康状态评估模型。首先选取关键传感器参数,进行归一量化处理并计算各参数权重。其次,将隶属度状态作为神经网络的输入进行训练,得到各子模块的输出,根据信息融合和决策规则得到具体健康状态等级。最后,通过具体算例验证该方法的可行性和有效性,以达到改善航空安全水平的目的。

Considering the multi-parameter,uncertainty and conflict of aero-engine condition monitoring data,a health condition assessment model based on D-S evidence theory and RBF neural network was established.Firstly,the key sensor parameters were selected,normalized and quantified,and the weights of each parameter were calculated.Secondly,the membership state was trained as the input of the neural network to get the output of each sub-module.According to the information fusion and decision-making rules,the specific health status grades were obtained.Finally,an example was given to verify the feasibility and effectiveness of the method in order to improve the aviation safety level.