液体火箭发动机启动过程实时在线故障检测算法

REAL TIME ON-LINE FAULT DETECTION ALGORITHM FOR STARTING PROCESS OF LIQUID PROPELLANT ROCKET ENGINES

关键词:  液体推进剂火箭发动机  故障检测  人工神经元网络  实时算法

Key words:  Liquid propellant rocket engine  Fault detection  Artificial neural network  Real time algorithm

摘要:

利用神经网络技术实现了液体火箭发动机启动过程的非线性辩识；提出并实现了一种基于辩识误差检验的故障检测策略。经大量实际发动机热试车数据验证表明，所提出的检测算法十分有效。由于算法所利用的监测参数均系实际发动机地面试车中所测量的参数，且检测算法在线工作时计算量十分小，因而所提出并实现的检测算法可以直接应用于工程实际。

Abstract:

he starting process and cutting off processe of liquid propellant rocket engines are inherent nonlinear stochastic ones. It is a very difficult task to carry out detecting faults in such processes. In this paper, based on system identification theory, Authors employ artificial neural networks technique to complete the nonlinear system identification for the starting process of the engine with trubopump system. The fault detection method based on checking identification error is proposed and implemented. The results of detecting faults, which are obtained from testing with a number of practical engine’s fire-test data, show that the fault detection algorithm proposed in the paper is very effective. Because the algorithm needs less computation cost, and the measured parameters that are required for the fault detection algorithm are consistent with ones for the current monitoring system on the ground test, the algorithm studied and implemented may directly be applied into real monitoring system of liquid propellant rocket engines.