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

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Proceedings of the 11th International Conference on Modelling, Identification and Control (ICMIC2019)

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 582))

Abstract

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.

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Author information

Authors and Affiliations

  1. China Datang Corporation Science and Technology Research Institute, DaTangThermal Power Technology Research Institute, Lugu Village Road West Two Jade Spring District 18 Building Shijingshan District, Beijing, China

    Yingmin Wang

  2. Inner Mongolia Datang International Tuoketuo Power Generation Co. Ltd., Hohhot, 010206, China

    Dong Cui & Feng Guo

Authors
  1. Yingmin Wang
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  2. Dong Cui
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  3. Feng Guo
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Corresponding author

Correspondence to Yingmin Wang .

Editor information

Editors and Affiliations

  1. College of Electronic Information and Automation, Civil Aviation University of China, Tianjin, China

    Rui Wang

  2. Department of Automation, Nankai University, Tianjin, China

    Zengqiang Chen

  3. Department of Automation, University of Science and Technology Beijing, Beijing, China

    Weicun Zhang

  4. Department of Engineering Design and Mathematics, University of the West of England, Bristol, UK

    Quanmin Zhu

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Wang, Y., Cui, D., Guo, F. (2020). A Detection and Isolation of Faults Technique in Automotive Engines Using a Data-Driven and Model-Based Approach. In: Wang, R., Chen, Z., Zhang, W., Zhu, Q. (eds) Proceedings of the 11th International Conference on Modelling, Identification and Control (ICMIC2019). Lecture Notes in Electrical Engineering, vol 582. Springer, Singapore. https://doi.org/10.1007/978-981-15-0474-7_48

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