Intelligent Fault Diagnosis of Marine Diesel Engines Based on Efficient Channel Attention-Improved Convolutional Neural Networks

Abstract

With the rapid development of smart ships, the ship maintenance model is also changing. In order to extract the fault characteristics of diesel engine thermal parameters more easily, reduce the model’s complexity and improve the model’s accuracy, a new approach is proposed: first, the traditional convolutional neural networks (improved convolutional neural networks (ICNN)) are improved by using Meta-ACON as the activation function, improved AdamP as the optimizer, and label smoothing regularization (LSR) as the loss function, which enhances the stability of the model. Secondly, efficient channel attention (ECA) is added to achieve the mastery of global feature information, reduce the complexity of the traditional self-attention module, and enhance the model’s feature extraction ability. Lastly, the accuracy and reliability of the model are verified through ablation and comparison experiments. The accuracy rate reaches 97.6%, which is significantly improved by 32.1% compared with the original model, and the robustness of the model is verified through the introduction of noise. The experimental results demonstrate the applicability of the model in the field of diesel engine fault diagnosis.