基于蚁群优化的长短时神经网络变外形飞行器故障模式识别

变外形飞行器机械结构复杂，在变外形过程中发生故障的概率大，传感器测量成本高，针对这些问题，提出了一种基于长短时神经网络进行飞行器测试故障诊断的方法。首先根据变外形飞行器的气动参数模型和非线性动力学模型，构建变外形飞行器执行机构故障特征数据库。然后针对变外形飞行器发生故障时的序列化特征数据，提出基于长短时神经网络的执行器故障诊断框架。利用蚁群优化算法对网络训练的超参数进行优化，提高故障诊断的准确性与泛化性。通过仿真验证了该方法可实现变外形飞行器的低成本、高效率、高精度的故障快速定位。

Fault Mode Recognition for Variable Shape Vehicles Based on ACO-LSTM

A method of missile test fault diagnosis based on the long and short time neural network is proposed for the problems of complex mechanical structure which has variable shape missile, high probability of failure in the process of variable shape and high cost of sensor measurement. First, an actuator fault characteristics database of the variable shape vehicle is constructed based on the aerodynamic parameter model and the nonlinear dynamics model of the variable shape vehicle. Then, an actuator fault diagnosis framework based on the long-short time neural network is proposed for the serialized feature data in the event of a failure of the variable shape vehicle. An ant colony optimization algorithm is used to optimize the hyperparameters of network training to improve the accuracy and generalization of fault diagnosis. It is verified through simulation that the method can achieve low-cost, high-efficiency, and high-accuracy fault rapid localization for variable-profile missiles.