To enhance the accuracy of fault diagnosis in diesel
engine fuel systems and address limited fault data availability, a diagnostic
method integrating association rules and one-dimensional convolutional
neural networks (1DCNN) was proposed. The simulation model of SC7H diesel
engine was established to simulate the typical four kinds of fuel system faults
under six operating conditions, including insufficient injection, excessive
injection, advanced injection timing, and delayed injection timing. The Apriori
algorithm was employed to transform fault data into association rules, enabling
effective data augmentation for 1DCNN training. Particle swarm optimization
algorithm was employed to optimize the minimum support and confidence
thresholds. The experimental results demonstrate that the proposed method
achieves a fault diagnosis accuracy of 91.67%, outperforming standalone
association rule classification models and 1DCNN models.
AI Yi, GUI Chengyu, CHEN Ziqiang, LIU Zhen, ZHANG Guodong, QIAO Xinqi
. Fault Diagnosis Method for Diesel Engine Fuel System Based on Association Rules and 1DCNN[J]. Vehicle Engine, 2025
, 0(6)
: 85
-91
.
DOI: 10.3969/j.issn.1001-2222.2025.06.013
