为适应柴油机远程自适应诊断的需要，提出了一种基于数据驱动的柴油机故障诊断方法，在国六柴油机EGR系统的典型故障中进行了验证。利用柴油机试验车队实时远程监测数据建立数据模型，将监测参数中与EGR工作密切相关的11个参数作为模型特征变量，首先采用核主成分分析法（Kernel Principal Component Analysis, KPCA)对原始高维数据进行降维，其次采用核Fisher判别分析法（Kernel Fisher Discriminant Analysis, KFDA)对柴油机EGR系统的正常数据和故障数据进行分类器训练，进而实现对未知数据的故障诊断。结果表明：此方法能够对试验中出现的两种EGR典型故障进行有效诊断，通过KPCA与KFDA相结合，改善了线性方法在处理柴油机故障变量的非线性及强耦合性问题上的缺陷。

In order to meet the requirements for the remote adaptive diagnosis of diesel engines, a data-driven fault diagnosis method was proposed and verified during the fault diagnosis of EGR system for China 6 diesel engine. Based on the real-time remote monitoring data of diesel engine test fleet, a data model was built and eleven parameters closely related to EGR were selected as the model characteristic variables. The dimension reduction of raw high-dimensional data was conducted by using the kernel principal component analysis (KPCA). Then the classifier training was carried out in respect of normal and fault data of EGR system by using the kernel fisher discriminant analysis (KFDA). In the end, the fault diagnosis was realized. The experimental results show that the proposed method can diagnose two typical faults of EGR system effectively and the combination of KPCA and KFDA solves the existing problems of dealing with nonlinearity and strong coupling of fault variable for diesel engine with the linear method.