A Detection and Isolation of Faults Technique in Automotive Engines Using a Data-Driven and Model-Based Approach

被引:1
|
作者
Wang, Yingmin [1 ]
Cui, Dong [2 ]
Guo, Feng [2 ]
机构
[1] China Datang Corp Sci & Technol Res Inst, DaTangThermal Power Technol Res Inst, Lugu Village Rd West Two Jade Spring Dist 18 Bldg, Beijing, Peoples R China
[2] Inner Mongolia Datang Int Tuoketuo Power Generat, Hohhot 010206, Peoples R China
来源
PROCEEDINGS OF THE 11TH INTERNATIONAL CONFERENCE ON MODELLING, IDENTIFICATION AND CONTROL (ICMIC2019) | 2020年 / 582卷
关键词
Fault detection; Fault isolation; Fault diagnosis; Data-driven; Diesel engine; Local linear model tree; NETWORK; DIAGNOSIS; SYSTEMS;
D O I
10.1007/978-981-15-0474-7_48
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Modern Diesel engines with exhaust gas recirculation have achieved a significant progress in intake system, fuel consumption and emissions. So the process became more complex. Therefore, fault detection and diagnosis is difficult to be done and need to be improved. This contribution shows a system of fault detection and diagnosis methods for diesel engines based on physical model and data-driven model. By applying physical dynamic process models, identification with local linear model tree (LOLIMOT), data-driven models and residuals are generated by parity equations. Measured data in fault-free operation is used to build data-driven models. Detectable deflections of these residuals lead to symptoms which are the basis for the detection of faults. In final applications look-up tables can be generated using data-driven models. Experiments with a diesel engine intake system on MATLAB have demonstrated the detection and diagnosis of faults is suitability for application with reasonable calculation effort.
引用
收藏
页码:503 / 520
页数:18
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