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摘要: 失火故障诊断是汽车车载诊断系统（On-board diagnostic，OBD）的重要组成部分，其直接关系到车辆行驶过程中的排放、燃油消耗和发动机损伤.本文对近年来国内外关于失火故障诊断方法的研究工作进行了系统性地总结和分析，重点介绍了汽车发动机失火故障诊断的判别依据、失火诊断方法分类、观测器设计等问题.最后对失火故障诊断的未来发展作了几点展望.Abstract: Misfire fault diagnosis is an important issue in on-board diagnosis (OBD) system, which has significant impacts on emissions, fuel consumption, and engine damage during vehicle movement. This study systematically reviews and summarizes the progress of misfire fault diagnosis methods, i.e., judgement basis, misfire diagnosis classification, and observer design. Finally, some perspectives on misfire fault diagnosis are also discussed.
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图 2 发动机失火故障诊断依据整体评价
Fig. 2 Overall evaluation of engine misfire fault diagnostic basis
表 1 基于数据的失火故障诊断方法评价
Table 1 Evaluation of misfire fault diagnosis methods based on data
特点 优点 缺点 1) 核心在于数据获取与处理分析数据特征, 寻找失火时的变化规律;
2) 信号处理有助于提高响应速度和故障诊断的精度;
3) 多种算法结合增加了计算的复杂度, 但提高了故障诊断精度;
4) 多缸失火难度较大, 精度难以保证, 有待进一步研究.1) 数据获取途径较多, 且较为可靠, 失火故障诊断精度较高;
2) 此类方法具有很大的延展性, 可应用于发动机不同故障的诊断方案.1) 数据量的大小对响应速度影响较大, 且应尽量涵盖所有可能的工况和故障类型, 不利于在线失火故障诊断;
2) 缺乏对数据本身物理意义的洞察, 且变工况情形使故障诊断精度降低, 实时性变差.
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表 2 基于模型的失火故障诊断方法评价
Table 2 Evaluation of misfire fault diagnosis methods based on model
特点 优点 缺点 1) 关键在于寻找具体的物理参数, 通过对参数的跟踪或估计, 以及对参数本身物理意义的理解, 达到失火诊断的目的;
2) 其过程的实现需得到参数模型与观测器模型之间的残差向量.1) 实时性好, 故障诊断精度较高, 有利于在线失火故障诊断的实现;
2) 提供了一个对参数本身物理意义认知的视野, 有利于对失火故障原因的分析.1) 发动机状态复杂性高, 模型本身存在不确定性, 导致系统状态监测难度加大;
2) 多缸失火故障诊断仍难度较大, 不能准确识别失火故障的发生原因.
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表 3 基于数据和模型结合的失火故障诊断方法评价
Table 3 Evaluation of misfire fault diagnosis methods based on data and model
特点 优点 缺点 结合了基于数据和模型的失火诊断方法的优点. 提高了失火故障诊断精度, 增强了鲁棒性, 更加适用于变工况等情形的失火故障诊断方法实现. 增大了诊断方法的复杂度, 计算负荷较大.
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