Resumen
High heat load on diesel engines is a main cause of ship failure, which can lead to ship downtime and pose a risk to personal safety and the environment. As such, predictive detection and maintenance measures are highly important. During the operation of marine diesel engines, operating data present strong dynamic, time lag, and nonlinear characteristics, and traditional models and prediction methods cause difficulties in accurately predicting the heat load. Therefore, the prediction of its heat load is a challenging and significant task. The continuously developing machine learning technology provides methods and ideas for intelligent detection and diagnosis maintenance. The prediction of diesel engine exhaust temperature using long short-term memory network (LSTM) is analyzed in this study to determine the diesel engine heat load and introduce an effective method. Spearman correlation coefficient method with the addition of artificial experience is utilized for feature selection to obtain the optimal input for the LSTM model. The model is applied to validate the ship data of the Shanghai Fuhai ship, and results show that the mean absolute percentage error (MAPE) of the model is lowest at 0.089. Compared with other models, the constructed prediction model presents higher accuracy and stability, as well as an optimal evaluation index. A new idea is thus provided for combining artificial knowledge experience with data-driven applications in engineering practice.