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.