摘要
针对柴油机颗粒捕集器功能失效的问题,应用GT-SUITE软件对柴油机颗粒捕集器进行仿真分析,研究颗粒捕集器温度、压降和碳烟浓度等与其状态的关联关系,并采用一维卷积神经网络对颗粒捕集器状态特征“自学习”,提高颗粒捕集器状态辨识的准确度。
According to the failure problem of the diesel particulate filter function,the authors simulate and analyze the diesel engine DPF through GT-SUITE software and study the relationship between the DPF state with its temperature,pressure drop,and soot concentration.Then,they improve the accuracy of DPF state identification through applying the one-dimensional convolutional neural network self-learning the state features.
作者
程德新
赵树恩
张军
王欣伟
胡超超
CHENG Dexin;ZHAO Shu’en;ZHANG Jun;WANG Xinwei;HU Chaochao(School of Mechatronics and Vehicle Engineering,Chongqing Jiaotong University,Chongqing 400074,China;State Key Laboratory of Engine Reliability,Weifang 261000,China;Weichai Power Co.,Ltd.,Weifang 261000,China)
出处
《客车技术与研究》
2022年第2期36-40,共5页
Bus & Coach Technology and Research
基金
内燃机可靠性国家重点实验室开放基金项目(SKLER-201912)。
关键词
柴油机
颗粒捕集器
故障特征
状态辨识
diesel engine
DPF
fault characteristics
state identification
