Multi-Dimensional Global Temporal Predictive Model for Multi-State Prediction of Marine Diesel Engines

Abstract

The reliability and stability of marine diesel engines are pivotal to the safety and economy of maritime operations. Accurate and efficient prediction of the states of these engines is essential for performance evaluation and operational continuity. This paper introduces a novel hybrid deep learning model, the multi-dimensional global temporal predictive (MDGTP) model, designed for synchronous multi-state prediction of marine diesel engines. The model incorporates parallel multi-head attention mechanisms, an enhanced long short-term memory (LSTM) with interleaved residual connections, and gated recurrent units (GRUs). Additionally, we propose a dynamic arithmetic tuna optimization algorithm, which synergizes tuna swarm optimization (TSO), and the arithmetic optimization algorithm (AOA) for hyperparameter optimization, thereby enhancing prediction accuracy. Comparative experiments using actual marine diesel engine data demonstrate that our model outperforms the LSTM, GRU, LSTM–GRU, support vector regression (SVR), random forest (RF), Gaussian process regression (GPR), and back propagation (BP) models, achieving the lowest root mean squared error (RMSE) and mean absolute error (MAE), as well as the highest Pearson correlation coefficient across three sampling periods. Ablation studies confirm the significance of each component in improving prediction accuracy. Our findings validate the efficacy of the proposed MDGTP model for predicting the multi-dimensional operating states of marine diesel engines.

Keywords

• marine diesel engine
• multi-state prediction
• multi-head attention mechanism
• LSTM
• GRU
• tuna swarm optimization