基于自组织神经网络的信息融合在故障诊断中的应用

Information Fusion Based on Self-organizing Neural Network in Fault Diagnosis

提出了一种用于某机舵面系统故障诊断的方法.应用自组织神经网络的非线性拟合能力扩展相关传感器的测量信息,采用D-S证据论算法将相关传感器的输出信息进行融合.信息融合诊断策略根据这些信息确定出故障,同时对故障信号进行识别.建立了某机舵面系统故障诊断的数学模型,并进行了计算机仿真.仿真实验结果表明,该故障诊断结构形式能够对舵面常见故障进行有效的识别和告警,显著地降低了故障诊断的不确定性,提高了故障模式的识别率.

A method is presented for fault diagnosis of an airplane steering surface system.The nonlinear fitting ability of self-organizing neural network is used to expand the information measured by related sensors,and output information of the related sensors is synthesized with D-S（Dempster-Shafer） proof algorithm.Based on the synthesized information,the information fusion diagnosis strategy can define the fault and identify the fault signals.The mathematical model of the airplane steering surface system fault diagnosis is constructed,and computer simulations are made.Simulation results show that this diagnosis structure can identify and warn the normal failures of the steering surface.It can significantly reduce the uncertainty of fault diagnosis and improve the recognition rate on fault modes.