基于遗传算法的大推力氢氧补燃发动机故障检测

Fault Diagnosis of High-Thrust LOX/LH2 Staged Combustion Cycle Engines Base on Genetic Algorithm

摘要:

通过优化故障检测的方法，可以提高大推力氢氧补燃发动机故障检测的精确性能。BP神经网络是基于梯度的方法确定权值与阈值，遗传算法是一种多点搜索的优化方式，具有良好的全局寻优能力，可以优化BP网络的不足。基于发动机试车过程中测得到的流量、温度、压力等信号，应用GA-BP神经络构建发动机工作过程的非线性辨识模型，对大推力氢氧补燃发动机可能的运行故障进行检测，由试车数据的仿真结果可知，该算法达到了较好的故障检测效果。

Abstract:

In order to improve the performance of fault detection of high-thrust LOX/LH2 staged combustion cycle engines, a prediction method is proposed. BP neural network is based on the gradient method to determine the weight and threshold, Genetic algorithm（GA） is a multi-point search optimization method, which has good global optimization ability and can optimize BP neural network. Based on the flow, temperature, pressure and other signals measured during the engine test run, the GA-BP neural network is used to construct a nonlinear identification model of the engine working process, to detect the possible operating faults of the high-thrust hydrogen-oxygen supplementary combustion engine. The simulation results show that the algorithm achieves a better fault detection effect.