. As the power source of the ship sailing, diesel engine will inevitably produce some thermal faults in the course of sailing, which will affect the stability of the ship sailing, so the diagnosis of thermal faults becomes very important. This paper uses Simulink software platform to simulate the thermal failure of diesel engine, and selects seven thermal parameters as the source of data set. The data set is input into LSTM neural network algorithm diagnosis model, several typical fault modes of diesel engine are output, and the data processing and image drawing are carried out in Matlab. Compared with other algorithms, LSTM neural network algorithm solves the long time dependence problem and has a high interpretation of the predicted data. The results show that the fault diagnosis model based on LSTM neural network algorithm can diagnose the diesel engine fault mode well.

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. In this paper, 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 are processed and the prediction image is plotted in MATLAB. LSTM neural network algorithm solves the problem of long-term dependence compared with other algorithms, and has a high degree of interpretation of the predicted data. The results show that the fault diagnosis model based on LSTM neural network algorithm can well diagnose the fault mode of ship power station.