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 safe operation of marine diesel engines (MDEs) is an important safeguard for ships and engine crews at sea. In this paper, a combined neural network prediction model (PCA-CNN-BiLSTM) is proposed for the problem of multi-parameter prediction and fault warning for MDEs. PCA is able to reduce the data dimensions and diminish the redundant information in the data, which helps to improve the training efficiency and generalization ability of the model. CNN can effectively extract spatial features from data, assisting in capturing local patterns and regularities in signals. BiLSTM works to process time series data and capture the temporal dependence in the data, enabling prediction of the failure conditions of MDE, condition monitoring, and prediction of a wide range of thermal parameters with more accuracy. We propose a standardized Euclidean distance-based diesel engine fault warning threshold setting method for ships combined with the standard deviation index threshold to set the diesel engine fault warning threshold. Combined with experimental verification, the method can achieve real-time monitoring of diesel engine operating condition and abnormal condition warning and realize diesel engine health condition assessment and rapid fault detection function.

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.