School of Intelligent Systems Engineering, Sun Yat-sen University, Guangzhou 510006, China.
Sensors (Basel). 2021 Jul 24;21(15):5026. doi: 10.3390/s21155026.
A real-time fault diagnosis method utilizing an adaptive genetic algorithm to optimize a back propagation (BP) neural network is intended to achieve real-time fault detection of a liquid rocket engine (LRE). In this paper, the authors employ an adaptive genetic algorithm to optimize a BP neural network, produce real-time predictions regarding sensor data, compare the projected value to the actual data collected, and determine whether the engine is malfunctioning using a threshold judgment mechanism. The proposed fault detection method is simulated and verified using data from a certain type of liquid hydrogen and liquid oxygen rocket engine. The experiment results show that this method can effectively diagnose this liquid hydrogen and liquid oxygen rocket engine in real-time. The proposed method has higher system sensitivity and robustness compared with the results obtained from a single BP neural network model and a BP neural network model optimized by a traditional genetic algorithm (GA), and the method has engineering application value.
利用自适应遗传算法优化反向传播（BP）神经网络的实时故障诊断方法旨在实现液体火箭发动机（LRE）的实时故障检测。在本文中，作者采用自适应遗传算法优化 BP 神经网络，对传感器数据进行实时预测，将预测值与实际采集的数据进行比较，并使用阈值判断机制判断发动机是否出现故障。利用某型液氢液氧火箭发动机的数据对所提出的故障检测方法进行了仿真验证。实验结果表明，该方法能够有效地对该型液氢液氧火箭发动机进行实时诊断。与单个 BP 神经网络模型和传统遗传算法（GA）优化的 BP 神经网络模型的结果相比，所提出的方法具有更高的系统灵敏度和鲁棒性，具有工程应用价值。
