A Deep Learning-Based Fault Warning Model for Exhaust Temperature Prediction and Fault Warning of Marine Diesel Engine

Abstract

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.

A Bibliometric Analysis and Overall Review of the New Technology and Development of Unmanned Surface Vessels

“…New technology with intelligence was also used in the health management of USVs, exemplified by a hybrid neural network (HNN) prediction model, which integrates Convolutional Neural Networks (CNN), Bidirectional Long Short-Term Memory (BiLSTM) and Attention mechanisms, designed specifically for the prediction of Exhaust Gas Temperature (EGT) in marine diesel engines. This approach provides a new way of thinking for the research of fault early warning and the health management of marine diesel engines [74]. To speed up autonomy, the Unmanned Maritime Systems Program Office (PMS 406) is enhancing autonomy in unmanned maritime vehicles through the Unmanned Maritime Autonomy Architecture (UMAA) for software standards and the Rapid Autonomy Integration Lab (RAIL) for developing new capabilities [75].…”

Synergising an Advanced Optimisation Technique with Deep Learning: A Novel Method in Fault Warning Systems

“…The concepts of automation and intelligent manufacturing have emerged as the driving force behind industrial development in the modern era [1]. As technological advancements lead to increasingly complex machinery and equipment being used on production lines, there is a growing demand for precision operation and efficiency in ever-changing production environments [2].…”

Research on Multi-Parameter Fault Early Warning for Marine Diesel Engine Based on PCA-CNN-BiLSTM

“…Liu et al combined the feature extraction capability of the attention mechanism and the time-series memory capability of the Long Short-Term Neural Network (LSTM) to construct an exhaust temperature prediction model for MDE and set the fault threshold for exhaust temperature prediction based on the distribution of residuals between the model's predicted value and the actual value using a process control method [18]. Ji et al proposed a hybrid neural network prediction model based on deep learning for MDE exhaust temperature (EGT) prediction and demonstrated that the proposed CNN-BiLSTM-Attention prediction accuracy is higher by comparing experiments with other neural network prediction models [19].…”