粗糙集与神经网络在航空发动机气路故障诊断中的应用

Aeroengine Gas Path Fault Diagnosis Using Rough Sets and Neural Networks

提出了一种基于粗糙集理论和神经网络集成的发动机智能故障诊断方法,首先对测量数据进行离散处理,并运用粗糙集理论建立故障决策表,进而约简属性和提取规则,对航空发动机气路部件的几种典型故障进行隔离。然后建立神经网络故障诊断子系统,使用粗糙集处理后的数据计算出发动机气路相关部件的故障程度。最后,还验证了粗糙集神经网络故障诊断系统的抗噪性能。研究表明,该系统能够正确而且高效地诊断出发动机故障的严重程度,并具备良好的抑制噪声的能力。

Aeroengine is a very complex nonlinear object. Traditional methods for its fault diagnosis were proved time-consuming and low efficient. A new system based on rough sets and neural networks for the fault diagnosis of aeroengine gas path component faults was presented in this paper. At first,the rough set theory was used to detect qualitatively faults and to isolate the fault. It consists of three steps.performing the discretion of sensed data ,establishing the decision table and generating rules. After that ,feed-forward neural networks are added into the system to construct several sub-systems,which take the engine sensible data pretreated by rough sets as inputs and compute damage degrees of the aeroengine fault. Finally, the noise rejection abilities of the engine fault diagnosis system were analyzed. The test results show that the system can quan titatively diagnose the faults of aeroengine gas path components precisely and efficiently ,while it is robust for noise rejection.