内燃机工程2026，Vol.47Issue(3)：40-50,11.DOI:10.13949/j.cnki.nrjgc.2026.03.005
基于改进EEMD与图像识别的氨中速发动机氨引燃失败及失火故障诊断
Fault Diagnosis of Ammonia Ignition Failure and Misfire in Medium-Speed Ammonia Engines Based on Improved EEMD and Image Recognition
摘要
Abstract
To diagnose the issues related to ammonia engine ignition failures and misfires caused by difficulties in ignition and unstable combustion,a diagnostic method based on instantaneous speed signals integrating improved ensemble empirical mode decomposition(EEMD)with image recognition technology was proposed.The instantaneous speed signal was decomposed into several intrinsic mode functions(IMFs)using improved EEMD.By combining fast Fourier transform(FFT)and Pearson correlation coefficient(PCC),the IMF 11 component sensitive to faults was selected to identify ammonia ignition failure(ammonia fuel fails to be ignited by the pilot diesel)and misfire(both diesel and ammonia fail to combust)faults.Furthermore,the IMF8 time-domain component was converted into polar coordinate images,and the lightweight convolutional neural network EfficientNet-B1 was employed for training,ultimately achieving precise localization of the cylinders with ammonia ignition failure and misfire.The results showed that under normal engine operation,the energy of IMF 11 was weak,whereas ammonia ignition failure or misfire faults significantly amplified this low-frequency component.The frequency-domain amplitude of IMF 11 could serve as a key feature for identifying ammonia ignition failure and misfire faults.The main frequency of the IMF8 component was closest to the rotational frequency of the ammonia engine.Converting the time-domain signal of the IMF8 component into two-dimensional polar coordinate images clearly revealed the firing phase information.The study demonstrates that the lightweight EfficientNet-B1 model enables precise localization of the faulty cylinders affected by ammonia ignition failure or misfire in medium-speed ammonia engines.关键词
氨发动机/引燃失败/失火/瞬时转速/集合经验模态分解/图像识别Key words
ammonia engine/ignition failure/misfire/instantaneous speed/ensemble empirical mode decomposition(EEMD)/image recognition分类
能源科技引用本文复制引用
张竹,刘赋泽,赵赫,李红梅,胡宇辰,胡闹,杨建国,谢良涛,张冠军,胡磊,余永华..基于改进EEMD与图像识别的氨中速发动机氨引燃失败及失火故障诊断[J].内燃机工程,2026,47(3):40-50,11.基金项目
国家自然科学基金项目(52271328) （52271328）
韶关南岭团队计划项目(240717137224701)National Natural Science Foundation of China(52271328) （240717137224701）
NanLing Talent Program of Shaoguan City(240717137224701) （240717137224701）
