基于 AGABP 神经网络的运载火箭推力偏移损失故障在线诊断

To address the thrust deviation loss fault in the launch vehicle’s power system, an online detection and diagnosis method for thrust faults based on the Adaptive Genetic Algorithm-based Back Propagation(AGABP)neural network is proposed. To achieve low-latency, high-precision online detection and diagnosis of thrust loss faults, this method solely utilizes the rocket motion information measured by onboard sensors. Firstly, a six-degree-of-freedom(6-DOF)modeling is established based on the data and thrust fault types of a certain type of launch vehicle in China. Historical state information sensitive to faults, such as overload and apparent acceleration, was used as inputs for network training. Secondly, the initial weights in the BP neural network are adjusted through the adaptive genetic algorithm to obtain optimized network parameters. Finally, the resulting online diagnostic model for thrust deviation loss faults in launch vehicles is verified through 6-DOF online simulations. Numerical simulation results indicate that compared with the traditional BP network, the AGABP-based method exhibits faster convergence speed with fewer iteration generations. The accuracy of fault location is 96. 51%, the fault location delay is between 0. 1 s and 2 s, and the difference between the predicted and actual thrust reduction degree is within 20% for 94. 19% of the samples.