基于LSTM神经网络算法的船舶电站故障诊断

Fault Diagnosis of Ship Power Station Based on LSTM Neural Network Algorithm

船舶电站是船舶中的重要组成部分,船舶电站的故障类型较为复杂。文章利用Simulink软件平台对船舶电站各种短路故障进行仿真建模,选取各相电流电压参数作为数据集的来源,并在MATLAB中进行数据的处理和预测图像的绘制。LSTM神经网络算法相比于其他算法,解决了长时依赖问题,并对预测数据有极高的解释度。结果表明:基于LSTM神经网络算法的故障诊断模型能够很好的对船舶电站故障模式做出诊断。

Ship power station is an important part of the ship,and the fault types of ship power station are complex.The Simulink software platform is used to simulate and model various short-circuit faults of ship power station.The current and voltage parameters of each phase are selected as the source of the data set,and the data processing and prediction image drawing are carried out in MATLAB.Compared with other algorithms,LSTM neural network algorithm solves the problem of long-term dependence,and has a high degree of interpretation for the prediction data.The results show that the fault diagnosis model based on LSTM neural network algorithm can diagnose the fault mode of ship power station well.