Research on Fault Early Warning of Marine Diesel Engine Based on CNN-BiGRU

Abstract

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.