Feifei Yu1, Yongxian Huang2,*, Guoyan Chen1, Xiaoqing Yang2, Canyi Du2,*, Yongkang Gong2
CMC-Computers, Materials & Continua, Vol.82, No.1, pp. 843-862, 2025, DOI:10.32604/cmc.2024.058051
- 03 January 2025
Abstract 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… More >
Login
Register
