Fault Diagnosis Method for Diesel Engine Fuel System Based on Association Rules and 1DCNN

doi:10.3969/j.issn.1001-2222.2025.06.013

Vehicle Engine ›› 2025, Vol. 0 ›› Issue (6) : 85-91. DOI: 10.3969/j.issn.1001-2222.2025.06.013

AI Yi¹，GUI Chengyu¹，CHEN Ziqiang¹，LIU Zhen²，ZHANG Guodong²，QIAO Xinqi¹

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Abstract

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.

Key words

association rule / convolutional neural network / diesel engine / fuel system / fault diagnosis

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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 https://doi.org/10.3969/j.issn.1001-2222.2025.06.013