基于动态云BP网络的液体火箭发动机故障诊断方法

将云模型与BP(back propagation)神经网络以串联方式有机结合,首先利用云变换方法进行网络的结构辨识和云模型的特征提取,同时通过在输入层引入单位延时环节描述发动机工作过程动态特性,研究提出了基于动态云BP网络的液体火箭发动机故障诊断方法.结合实际试车数据的验证结果表明,该方法能够准确识别发动机已有的3种故障模式,通过在试车数据中添加0期望、0.2标准差的随机噪声的方法来模拟环境噪声和测试过程中产生的随机噪声,根据持续性原则,方法仍能够正确进行故障检测与分类.方法单步运行时长为1.124×10-4s,完全能够满足实时性要求. 

Fault diagnosis method for liquid-propellant rocket engines based on the dynamic cloud-BP neural network

A fault diagnosis method for liquid-propellant rocket engines was proposed based on the dynamic cloud-BP(back propagation) neural network in the way of the integration of cloud model and BP neural network. The Cloud transform method was used to identify the network configuration and to extract the cloud features. And a unit time-delay was also introduced into the input layer to describe the dynamic characteristics of the engine. Results with test data show that the method can isolate the existed 3 fault modes precisely. A 0 expectation, 0.2 standard deviation noise was used to simulate the entironmental noise and stochastic noise, and the method can still detect and classify the fault accurately acount to lasting-rule. The method can run in real-time with the single processing time being 1.124×10^-4s.