一种基于自适应神经网络的航空发动机故障诊断方法

Fault diagnosis of aero-engine based on self-adaptive neural network

航空发动机在使用过程中,气路部件的性能不可避免地发生了蜕化,相应的故障诊断技术对发动机的健康管理系统具有重要意义.本文针对发动机在设计点的非线性部件级模型,借助PCC(Pearson correlation coefficient)相关分析方法,对神经网络的输入参数和输出参数的选取方式进行了优化.以前馈型神经网络为基础,针对常规BP(back propagation)神经网络收敛速度不稳定、且容易陷入极小值的缺陷,设计了一种新的自适应神经网络,准确估计了发动机部件的蜕化情况.这种算法融合了比例因子和动量因子,改善了网络的学习速率,提高了神经网络置信度和对发动机模型参数的泛化能力.结果表明,本文设计的自适应神经网络的精度优于常规BP神经网络,并且在训练样本数较少时,依然能够通过训练得到理想的网络,保证发动机健康参数的故障检测具有较高精度.

As the gas path components＇ performance cannot avoid degeneration while the aero-engine is working, the related fault diagnosis technology is of great meaning to the aero-engine＇s health management system. Aiming at the aero-engine＇s nonlinear model at the design point, this paper optimizes the selection rules of the neural network＇s input parameters and output parameters through relative analysis of PCC（Pearson correlation coefficient）. For the regular BP（back propagation） neural network＇s weakness of an unstable convergence speed and easy to fall into minimum value, this paper designs a new method via self-adaptive neural network that can accurately estimate the degeneration level of the aero-engine. This method combines scale factor and momentum factor, improves the learning speed of network and enhances the confidence level of neural network as well as the generalization ability of engine＇s model parameters. The results prove that the self-adaptive neural network method proposed in this paper has better accuracy than the regular BP neural network, and when the training samples are not too many, the training can also produce an ideal network, which guarantees a good accuracy for the fault diagnosis of the aero-engine＇s health parameters.