A Dual-Path CNN-MLP-CBAM Network for Intelligent Fault Diagnosis of Marine Diesel Engines

The reliability of marine diesel engines is crucial to the safety of ships. However, condition monitoring data under actual working conditions often exhibit multi-source heterogeneity, including frequency-domain features of vibration signals and statistical indicators of cylinder pressure, and are often constrained by class imbalances and strong noise interference. To address these challenges, this paper proposes a dual-path fusion diagnostic model CNN-MLP-CBAM, which integrates a one-dimensional convolutional neural network (CNN), a multilayer perceptron (MLP), and a convolutional block attention module (CBAM). The core innovation of this study lies in constructing an asymmetric dual-path structure: it breaks the limitation of conventional single-network input, utilises dedicated branches to process time-series waveforms and statistical features in parallel, and then performs deep feature fusion; meanwhile, adaptive synthetic sampling (ADASYN) is introduced to generate minority class samples to eliminate data bias. Experimental results on a public diesel engine fault dataset indicate that the model achieves diagnostic accuracies of 99.43% and 99.71% at 15db and 30db signal-to-noise ratios, respectively. Compared with mainstream baseline models such as LSTM and SVM, this method improves diagnostic accuracy by more than 5% under strong noise conditions, demonstrating outstanding robustness and potential for engineering applications.