基于人工神经网络的发射塔平台设备故障诊断

Fault Diagnosis of Launching Tower Platform Equipment Based on Artificial Neural Network

发射塔平台设备的稳定运行是航天发射任务成功的前提,因此需要对平台设备的状态进行监视,基于设备平台参数进行故障诊断和预测。本文给出了发射塔平台设备的数据采集方案,提出了基于BP人工神经网络的发射塔平台设备故障状态诊断方法,基于历史数据训练形成了双层神经网络模型。仿真结果表明,该方法能够提高故障诊断的效率,准确定位设备故障,消除发射塔平台设备事故隐患。

The stable operation of the tower platform equipment is the prerequisite for the success of the space launch mission.Therefore, it is necessary to monitor the status of the platform equipment, and perform fault diagnosis and prediction based on the equipment platform parameters. In this paper, the data acquisition scheme of the tower platform equipment is given, and the fault state diagnosis method of the tower platform equipment based on the BP artificial neural network is proposed, and a two-layer neural network model is formed based on the historical data training. The simulation results show that this method can improve the efficiency of fault diagnosis, accurately locate equipment faults, and eliminate hidden dangers of equipment accidents on the tower platform.