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

  • Yingmin WangEmail author
  • Dong Cui
  • Feng Guo
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 582)


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.


Fault detection Fault isolation Fault diagnosis Data-driven Diesel engine Local linear model tree 


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Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.China Datang Corporation Science and Technology Research Institute, DaTangThermal Power Technology Research InstituteBeijingChina
  2. 2.Inner Mongolia Datang International Tuoketuo Power Generation Co. Ltd.HohhotChina

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