The normal operation of the marine diesel engine is of great significance to ensure the normal navigation of the ship. Predicting its operation state and judging whether the diesel engine is in the abnormal state in advance can guarantee the safe navigation of the vessel. In this paper, combining the feature extraction ability of the convolutional neural network (CNN) and the time series data prediction ability of the bidirectional gated recurrent unit (BiGRU), a marine diesel engine exhaust temperature prediction model is constructed. The results show that the mean square error (MSE) of the prediction model is 0.1156, the average absolute error (MAE) is 0.2501, and the average absolute percentage error (MAPE) is 0.0005336. Then, according to the residual distribution between the predicted value and the actual value of the model output and the standard deviation of the residual calculated by using the sliding window, we set the alarm threshold, where the upper limit of residual error is 1 and the lower limit is 1. The upper limit of the standard deviation is 0.604. Finally, we used the data set under abnormal conditions for experimental verification. The results show that the method can accurately determine the fault early warning of the marine diesel engine and provides a new reference for the health management of intelligent marine equipment.

Marine diesel engines are essential for safe navigation. By predicting the operating conditions of diesel engines, the performance of marine diesel engines can be improved, failures can be prevented to reduce maintenance costs, and emissions can be controlled to protect the environment. To this end, this paper proposes a hybrid neural network (HNN) prediction model (CNN-BiLSTM-Attention) based on deep learning (DL) for predicting the exhaust gas temperature (EGT) of marine diesel engines. CNN is used to extract features from time-series data, BiLSTM is used to predict the time series through modeling, and Attention is used to improve the accuracy and robustness of fault prediction. Moreover, through comparison experiments with other neural network prediction models, it has been proven that the CNN-BiLSTM-Attention method is more accurate. This article also presents an approach to fault prediction by integrating the Mahalanobia distance and the mathematical model. Based on the Mahalanobia distance between the prediction result and the actual value, the function mapping method combined with the criterion is used to set the alarm value and threshold of the monitoring indicators, and the failure data set is used for experimental verification. The results indicate that the approach presented in this article can accurately realize the operating condition monitoring and fault early warning of marine diesel engines, which provides a new way of thinking for the research of fault early warning and health management of marine diesel engines.

The marine dual-fuel engine can switch between diesel and gas modes according to the requirements of sailing conditions, fuel cost, and other working conditions to make sure the ship is in the best operating condition. In fuel-mode switching in engines, problems such as unstable combustion and large speed fluctuations are prone to occur. However, there are some disadvantages, such as poor safety, environmental pollution, and easy damage to the engine, when the large, marine dual-fuel engine is directly tested on the bench. Therefore, in this paper, a joint simulation model of a dual-fuel engine is built using GT Power and MATLAB/Simulink to investigate the engine’s transient process of fuel-mode switching, and the conventional fuel PID(Proportion Integral Differential) control system is optimized using the cuckoo search (CS) algorithm. The simulation results show that the dual-fuel engine model has good accuracy, and the response in transient conditions meets the manufacturer’s requirements. In the process of switching from gas mode to diesel mode, due to the rapid change in fuel, the engine parameters, such as speed, fluctuate significantly, which is prone to safety accidents. In the process of switching from diesel to gas mode, because the fuel switching is gentle, all parameters are relatively stable, and the possibility of safety accidents is slight. The fuel PID control system optimized based on the cuckoo search algorithm has a better engine control effect than the traditional fuel control system.