Engine Misfire Fault Detection Based on the Channel Attention Convolutional Model

To accurately diagnose misfire faults in automotive engines, we propose a Channel Attention Convolutional Model, specifically the Squeeze-and-Excitation Networks (SENET), for classifying engine vibration signals and precisely pinpointing misfire faults. In the experiment, we established a total of 11 distinct states, encompassing the engine’s normal state, single-cylinder misfire faults, and dual-cylinder misfire faults for different cylinders. Data collection was facilitated by a highly sensitive acceleration signal collector with a high sampling rate of 20,840 Hz. The collected data were methodically divided into training and testing sets based on different experimental groups to ensure generalization and prevent overlap between the two sets. The results revealed that, with a vibration acceleration sequence of 1000 time steps (approximately 50 ms) as input, the SENET model achieved a misfire fault detection accuracy of 99.8%. For comparison, we also trained and tested several commonly used models, including Long Short-Term Memory (LSTM), Transformer, and Multi-Scale Residual Networks (MSRESNET), yielding accuracy rates of 84%, 79%, and 95%, respectively. This underscores the superior accuracy of the SENET model in detecting engine misfire faults compared to other models. Furthermore, the F1 scores for each type of recognition in the SENET model surpassed 0.98, outperforming the baseline models. Our analysis indicated that the misclassified samples in the LSTM and Transformer models’ predictions were primarily due to intra-class misidentifications between single-cylinder and dual-cylinder misfire scenarios. To delve deeper, we conducted a visual analysis of the features extracted by the LSTM and SENET models using T-distributed Stochastic Neighbor Embedding (T-SNE) technology. The findings revealed that, in the LSTM model, data points of the same type tended to cluster together with significant overlap. Conversely, in the SENET model, data points of various types were more widely and evenly dispersed, demonstrating its effectiveness in distinguishing between different fault types.